JP2006268784A - System for predicting power line accident - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a system for predicting a power line accident, which may predict the power line accident caused by interaction between the natural phenomenon and trees near power lines without a patrol by an employee. <P>SOLUTION: The system for predicting the power line accident caused by the natural phenomenon, comprises a map database 20 for storing map data 21, which includes a data for the power lines; a tree growth database 30 for storing a data for tree growth 31, by types of trees; a tree database 40 for storing a tree data 41; an accident probability database 50 for storing the accident probability data 51; a growth simulation means 60 for simulating the tree growth near the power lines; a prediction means 70 for predicting a tree disaster; and a means 80 for calculating the power line accident probability caused on the power lines based on a data associated with a tree causing the disaster and the natural phenomenon as well as the disaster probability data 51. <P>COPYRIGHT: (C)2007,JPO&INPIT

Description

  The present invention relates to a system for predicting a disaster that occurs in a distribution line due to an interaction between a tree near the distribution line and a natural phenomenon.

  Transmission lines installed outdoors may cause serious disasters such as cutting due to contact with trees near the transmission lines due to gusts or snowfall. When such a disaster occurs in the transmission line, it is natural to restore it as soon as possible, but it is necessary to prevent such a disaster in advance.

  As a method for preventing such a disaster, conventionally, when an electric power company employee or the like periodically circulates the distribution line network, for example, when an approach of a typhoon or snowfall is predicted, it affects the distribution line. A tree having a high probability of affecting or affecting is found, and such a tree has been cut down. However, there is a problem that it takes a lot of time and a lot of money to make the employees go around the distribution line network and discover trees to be cut. In addition, since natural phenomena such as snowfall can only be predicted in a short period of time, which can be predicted a few days ago, it is not possible for employees to go around all distribution networks after snowfall is predicted. There was a problem that it was impossible.

  On the other hand, methods for predicting the amount of snowfall on the distribution line itself during snowfall have been proposed (see, for example, Patent Documents 1 to 3), but prevent snow damage caused only by the amount of snowfall on the distribution line itself. There is a problem that it is impossible to predict a disaster that occurs in a distribution line due to an interaction between a tree near the distribution line and a natural phenomenon.

Japanese Patent Laid-Open No. 06-105428 Japanese Patent Application Laid-Open No. 07-167966 Japanese Patent Application Laid-Open No. 08-194069

  In view of such circumstances, the present invention provides a distribution line disaster prediction system that predicts a disaster that occurs in a distribution line due to an interaction between a tree adjacent to the distribution line and a natural phenomenon without an employee or the like visiting the distribution line. This is the issue.

  A first aspect of the present invention for solving the above problem is a distribution line disaster prediction system for predicting a disaster that occurs in the distribution line due to an interaction between a tree adjacent to the distribution line and a natural phenomenon. A map database for storing map data including data relating to electric wires; a growth database for each tree for storing growth data for each tree, which is growth data divided according to the type of tree; and tree data for trees adjacent to the distribution lines. Based on at least the tree data and the tree-specific growth data, the tree database to be stored, the disaster probability database for storing the disaster probability data of the disaster that occurs in the distribution line due to the interaction between the tree and the natural phenomenon A growth simulation means for performing a growth simulation of a tree adjacent to a distribution line; and the growth simulation Disaster tree predicting means for predicting a disaster tree that may cause the distribution line disaster based on the distribution line, and distribution occurring in the distribution line based on at least the disaster tree, the data on the natural phenomenon, and the disaster probability data A distribution line disaster prediction system comprising distribution line disaster probability calculation means for calculating electric line disaster probability data.

  In the first aspect, it is possible to predict a distribution line disaster that occurs in the distribution line due to the interaction between the tree adjacent to the distribution line and the natural phenomenon, even if the employee or the like does not visit the site.

  According to a second aspect of the present invention, in the first aspect, there is further provided tree data registration / modification means for registering new tree data in the tree database or changing existing tree data stored in the tree database. It is in the distribution line disaster prediction system characterized by comprising.

  In the second aspect, more accurate tree data can be used, and therefore distribution line disaster can be predicted more accurately.

  According to a third aspect of the present invention, in the first or second aspect, the distribution system further comprises a distribution line disaster record database storing the past distribution line disaster probability data and the past distribution line disaster record. It is in the electric wire disaster prediction system.

  In the third aspect, the distribution line disaster probability data is calculated more accurately by comparing the past distribution line disaster probability data and the past distribution line disaster record with the calculated distribution line disaster probability data. Can do.

  A fourth aspect of the present invention is the distribution line disaster prediction system according to any one of the first to third aspects, wherein the natural phenomenon is snowfall and the disaster is snow damage.

  In the fourth aspect, it is possible to predict the distribution line snow damage caused in the distribution line due to the trees adjacent to the distribution line and the snowfall, without employees traveling around the site.

  A fifth aspect of the present invention is the distribution line disaster prediction system according to any one of the first to third aspects, wherein the natural phenomenon is wind and the disaster is wind damage.

  In the fifth aspect, it is possible to predict the distribution line wind damage caused in the distribution line due to the trees and the wind in the vicinity of the distribution line, even if the employee or the like does not visit the site.

  According to the present invention, it is possible to predict a distribution line disaster that occurs in a distribution line due to the interaction between a tree near the distribution line and a natural phenomenon without employees visiting the site. Can be efficiently prevented.

  The best mode for carrying out the present invention will be described below with reference to the drawings. The description of the present embodiment is an exemplification, and the configuration of the present invention is not limited to the following description.

(Embodiment 1)
FIG. 1 is a schematic diagram of a distribution line disaster prediction system according to Embodiment 1 of the present invention. As shown in FIG. 1, a distribution line disaster prediction system 1 according to the present embodiment is connected to a terminal 100 for inputting a position where a distribution line disaster is predicted and the terminal 100 via a network 5 and is distributed to the distribution line. And a distribution line disaster prediction server 10 that predicts a distribution line disaster that occurs in the distribution line due to the interaction between a tree and a natural phenomenon. The distribution line disaster prediction server 10 stores a map database 20 that stores map data 21 that includes data related to distribution lines, and a growth database by tree that stores growth data 31 by tree, which is growth data divided by tree type. 30, a tree database 40 for storing tree data 41 such as the type and size of the tree adjacent to the distribution line, and the distance between the distribution line and the tree, and the disaster that occurs in the distribution line due to the interaction between the tree and the natural phenomenon A disaster probability database 50 for storing disaster probability data 51, a growth simulation means 60 for performing a growth simulation of a tree adjacent to the distribution line based on the tree data 41 and the tree-specific growth data 31, and a distribution line based on the growth simulation Disaster tree prediction means 70 for predicting a disaster tree that may cause a disaster, and disaster ; And a distribution line accident probability calculation unit 80 for calculating a distribution line accident probability data 81 generated in the distribution line on the basis of data on disaster trees and natural phenomena obtained and the disaster probability data 51 by wood predicting unit 70.

  On the other hand, the terminal 100 includes a distribution line disaster prediction position specifying unit 110 that specifies a place where a distribution line disaster is predicted, and a tree data registration / modification unit 120 for registering / changing the tree data 41.

  Below, the structure of the distribution line disaster prediction server 10 and the terminal 100 is demonstrated concretely. Note that the distribution line disaster prediction server 10 and the terminal 100 do not need to be dedicated devices, and may be general personal computers or the like.

  First, the distribution line disaster prediction server 10 will be described. The map database 20 included in the distribution line disaster prediction server 10 stores map data 21. Specifically, the map data 21 is obtained by adding data relating to a distribution line as shown in FIG. 2 to normal map data including, for example, latitude, longitude, altitude, and structures. Here, the distribution line number in FIG. 2 is a unique identification number assigned to the distribution line. The start point position indicates the start point of the distribution line, and the end point position indicates the end point of the distribution line. That is, the distribution line is shown as a straight line connecting the start point position and the end point position.

  The tree growth database 30 stores tree growth data 31. Specifically, the tree-specific growth data 31 is, for example, data as shown in FIG. Here, the tree type indicates the type of the tree, the vertical direction indicates the size of the tree growing vertically in one year, and the horizontal direction indicates that the tree grows horizontally in one year. Indicates the size.

  The tree database 40 stores tree data 41. The tree data 41 is specifically data as shown in FIG. 4, for example. Here, the tree number is an identification number unique to the tree, the tree type indicates the type of the tree adjacent to the distribution line, the position indicates the position where the tree grows, and the horizontal distance indicates the tree. The horizontal distance between the branch of the tree and the distribution line closest to the tree is vertical, and the vertical distance is the vertical distance between the branch of the tree and the distribution line closest to the tree. The horizontal distance and the vertical distance can be obtained by various methods such as actual measurement, measurement using a laser, and measurement by analyzing aerial photographs. In the following, the position (A1, B1) is located in the vicinity of the position (X1, Y1) shown in FIG. 2, and the position (A2, B2) is located in the vicinity of the position (X2, Y2) shown in FIG. Shall.

The disaster probability database 50 stores disaster probability data 51. The disaster probability data 51 is specifically snow damage probability data as shown in FIG. 5, for example. The disaster probability data 51 indicates the disaster probability with respect to the amount of snowfall. For example, when the type of tree is “bamboo” and the amount of snowfall is 12 mm, the probability of occurrence of a distribution line disaster due to the interaction between the bamboo and snowfall is “5%”. In this embodiment, the disaster probability data 51 based on the amount of snowfall (mm) is used. However, the disaster probability data 51 based on the weight of snowfall (kg / m ² ) may be used. Disaster probability data 51 based on the data may be used.

  The growth simulation means 60 functions to perform a growth simulation of a tree adjacent to the distribution line based on the tree data 41 and the tree-specific growth data 31. Specifically, as shown in FIG. 3, for example, “bamboo” grows 5 m in the vertical direction and 2 m in the horizontal direction in one year, so these numbers are divided by 365, which is the number of days in a year, The growth simulation of “bamboo” after a predetermined number of days can be performed by multiplying by the number of days. Similar growth simulations can be performed for other types of trees. In this embodiment, the tree growth simulation is performed by a simple method based on the tree data 41 and the tree-specific growth data 31. However, the environment in which each tree grows, such as soil, temperature, and sunshine duration, is considered. However, the tree growth simulation may be performed using a more precise method.

  The disaster tree predicting means 70 functions to predict a disaster tree that may cause a distribution line disaster based on a growth simulation. Specifically, for example, when the vertical distance and horizontal distance between the distribution line and the tree are both 0 or less are set as disaster trees, the predicted disaster day is exactly 5 on the day when the tree data 41 is acquired. If it is assumed that it is a year later, when a disaster tree near the wiring of the distribution line number “1200001” shown in FIG. 2 is predicted, the distribution tree number “1200001” of FIG. Trees that grow at the positions (A1, B1) and (A2, B2) that are close to the straight line connecting them from FIG. 4 by extracting the start point position (X1, Y1) and end point position (X2, Y2) of the distribution line The numbers “0020101” and “0020102” are extracted. Then, the vertical distance and the horizontal distance between the distribution line and the trees on the day of the predicted disaster are obtained by growth simulation for the trees corresponding to the respective tree numbers.

  First, considering the tree with the tree number “0020101”, the vertical distance and horizontal distance between the tree with the tree number “0020101” and the distribution line are “5 m” and “10 m”, respectively, and the tree type is “ It turns out that it is "bamboo". Further, it can be seen from FIG. 3 that the vertical growth data 31 for the “bamboo” tree number “0020101” is “5 m” and the horizontal growth data 31 for the tree is “2 m”. Therefore, the vertical distance between the tree having the tree number “0020101” and the distribution line on the predicted disaster day (just five years after the date when the tree data 41 is acquired) is less than “0 m”, and the horizontal distance is “0 m”. Therefore, it can be predicted that the tree having the tree number “0020101” is a disaster tree. Next, when the growth simulation is similarly performed on the tree with the tree number “0020102”, the vertical distance between the tree with the tree number “0020102” and the distribution line on the predicted disaster day is “1 m”, and the horizontal distance is “ 0m ". Therefore, it can be predicted that the tree having the tree number “0020102” on the predicted disaster day is not a disaster tree. In this embodiment, a tree in which the vertical distance and horizontal distance between the distribution line and the tree are both 0 or less is set as a disaster tree, but a tree in which either one is 0 or less is also designated as a disaster tree. Well, disaster trees may be predicted using yet another criterion.

  In this way, the disaster tree predicting means 70 can predict a disaster tree that may cause a distribution line disaster.

  Distribution line disaster probability calculation means 80 calculates distribution line disaster probability data 81 generated in the distribution line based on the amount of snowfall and disaster probability data 51 which are data related to the disaster tree and natural phenomena obtained by the disaster tree prediction means 70. To function. Specifically, for example, assuming that the amount of snowfall on the predicted disaster day is “12 mm”, the tree having the tree number “0020101” is predicted to be a disaster tree by the disaster tree prediction unit 70. 4 indicates that the type of the tree having the tree number “0020101” is “bamboo”, and FIG. 5 indicates that the disaster probability data 51 by the tree having the tree number “0020101” is “5%”. Similarly, the disaster probability data 51 is obtained for all trees predicted to be disaster trees by the disaster tree prediction means 70, and the disaster probability data 51 having the highest disaster probability is extracted. The distribution line disaster probability data 81 can be obtained. In the present embodiment, the distribution probability probability data 81 has the highest disaster probability among the disaster probability data 51 of all the trees predicted to be disaster trees, but is predicted to be a disaster tree. The average of the disaster probability data 51 of all the trees may be used as the distribution line disaster probability data 81, and each of the extracted disaster probability data 51 is multiplied by a predetermined coefficient, and the distribution line having the highest disaster probability is distributed. As the disaster probability data 81, the average of the disaster probability data 51 multiplied by a predetermined coefficient may be used as the distribution line disaster probability data 81.

  Next, the terminal 100 will be described. The distribution line disaster predicted position specifying unit 110 constituting the terminal 100 functions to specify a place where the distribution line disaster is predicted. Specifically, for example, by manually inputting the distribution line number shown in FIG. 2 to the terminal 100, the location where the distribution line disaster is predicted is specified, or a map including the distribution network is displayed on the screen of the terminal 100 The location where the distribution line disaster is predicted is specified by designating the position on the map. The location where the distribution line disaster is predicted by the latter method can more easily identify the place where the distribution line disaster is predicted.

  The tree data registration / correction means 120 functions to register new tree data 41 in the tree database 40 of the distribution line disaster prediction server 10 or corrects the tree data 41 already registered in the tree database 40. To function. Specifically, for example, new tree data 41 manually input via the keyboard is registered in the tree database 40 of the distribution line disaster prediction server 10, or the tree data 41 and the keyboard erroneously registered in the tree database 40 are used. The correct tree data 41 that is manually input via is exchanged. Since the tree data registration / correction means 120 can use more accurate tree data 41, distribution line disaster can be predicted more accurately.

  The network 5 is not particularly limited as long as it can connect the terminal 100 and the distribution line disaster prediction server 10, and may be a dedicated line, a telephone line, the Internet, or the like.

  Next, operation | movement of the distribution line disaster prediction system 1 which concerns on this embodiment is demonstrated using FIG. FIG. 6 is a diagram illustrating a sequence of the distribution line disaster prediction system 1 according to the first embodiment.

  First, when a distribution line disaster prediction position 111 (for example, a position on a map) where a distribution line disaster is predicted is input to the terminal 100 by the distribution line disaster prediction position specifying unit 110 (S1), the distribution line disaster prediction is performed. The position 111 is transmitted to the distribution line disaster prediction server 10 via the network 5 (S2).

  When the distribution line disaster prediction position 111 is received by the distribution line disaster prediction server 10, the map data 21 including the distribution line disaster prediction position 111 is extracted from the map database 20 based on the distribution line disaster prediction position 111. (S3) The distribution line number of the distribution line located in the vicinity of the distribution line disaster predicted position 111 is extracted from the map data 21 (S4). Then, based on the distribution line number, tree data 41 adjacent to the distribution line corresponding to the distribution line number is extracted from the tree database 40 (S5), and corresponding to the tree data 41 from the tree-specific growth database 30. The tree-specific growth data 31 is extracted (S6).

  Next, based on the extracted tree data 41 and the tree-specific growth data 31, the growth simulation means 60 and the disaster tree prediction means 70 use the trees adjacent to the distribution lines located near the distribution line disaster predicted position 111. A disaster tree is predicted from (S7).

  Then, the distribution line disaster probability calculation means 80 extracts the disaster probability data 51 from the disaster probability database 50 based on the tree data 41 of the disaster tree (S8). Next, the highest disaster probability data 51 among all the extracted disaster trees is extracted as distribution line disaster probability data 81 (S9). The extracted distribution line disaster probability data 81 is transmitted to the terminal 100 together with the map data 21 including the distribution line disaster predicted position 111 via the network 5 (S10).

  When the distribution line disaster probability data 81 and the map data 21 are received by the terminal 100, the distribution line disaster probability data 81 is displayed on the display screen of the terminal 100 together with the map data 21 including the distribution line disaster predicted position 111 (S11). .

  In this way, it is possible to predict a distribution line disaster (distribution line snow damage) that occurs in the distribution line due to a tree adjacent to the distribution line and a natural phenomenon (snowfall) without employees traveling around the site.

(Embodiment 2)
In the first embodiment, the distribution line disaster probability due to snow damage targeting snowfall as a natural phenomenon is predicted. However, instead of the snow damage probability data, the distribution line due to wind damage targeting wind as a natural phenomenon is used. Disaster probability data may be predicted. The wind damage probability data is specifically an example of disaster probability data 51 as shown in FIG. This wind damage probability data indicates the distribution line disaster probability with respect to the wind speed. For example, when the type of tree is “cedar” and the wind speed is “18 m / s”, the disaster probability data 51 that causes a distribution line disaster due to the cedar is “12%”.

  By using such wind damage probability data in place of the snow damage probability data of the first embodiment, it is possible to connect the distribution lines with trees and natural phenomena (wind) in the vicinity of the distribution lines, even without employees visiting the site. The distribution line disaster (distribution line wind damage) that occurs can be predicted.

(Other embodiments)
The distribution line disaster prediction server 10 of the distribution line disaster prediction system 1 according to the first embodiment may further include a distribution line disaster record database that stores past distribution line disaster probability data 81 and past distribution line disaster records. By comparing the past distribution line disaster probability data 81 and the past distribution line disaster record with the calculated distribution line disaster probability data 81, more accurate distribution line disaster probability data 81 can be calculated.

  Further, when the distribution line disaster probability data 81 is displayed together with the map data 21 including the distribution line disaster predicted position 111 on the terminal 100 of the first embodiment, the distribution on the map data 21 according to the distribution line disaster probability data 81 is displayed. You may make it display by changing the color of the electric wire disaster prediction position 111. FIG. Specifically, for example, when the distribution line disaster probability data 81 exceeds 90%, the color of the distribution line disaster predicted position 111 on the map data 21 is displayed in red, and the distribution line disaster probability data 81 is 60 to 90. In the case of%, it may be displayed in yellow. By changing the color of the distribution line disaster predicted position 111 on the map data 21, it is possible to visually understand a place where a distribution line disaster is likely to occur.

It is the schematic of the distribution line disaster prediction system which concerns on Embodiment 1 of this invention. It is a figure which shows the map data which concern on Embodiment 1 of this invention. It is a figure which shows the growth data according to tree concerning Embodiment 1 of this invention. It is a figure which shows the tree data which concern on Embodiment 1 of this invention. It is a figure which shows the snow damage probability data which is an example of the disaster probability data which concern on Embodiment 1 of this invention. It is a figure which shows the sequence of the distribution line disaster prediction system which concerns on Embodiment 1 of this invention. It is a figure which shows the wind damage probability data which is an example of the disaster probability data which concern on Embodiment 2 of this invention.

Explanation of symbols

DESCRIPTION OF SYMBOLS 1 Distribution line disaster prediction system 10 Distribution line disaster prediction server 20 Map database 21 Map data 30 Growth database classified by tree 31 Growth data classified by tree 40 Tree database 41 Tree data 50 Disaster probability database 51 Disaster probability data 60 Growth simulation means 70 Disaster tree prediction Means 80 Distribution line disaster probability calculation means 81 Distribution line disaster probability data 100 Terminal 110 Distribution line disaster predicted position specifying means 111 Distribution line disaster predicted position 120 Tree data registration / correction means

Claims (5)

A distribution line disaster prediction system that predicts a disaster that occurs in the distribution line due to an interaction between a tree and a natural phenomenon adjacent to the distribution line,
A map database for storing map data including data on the distribution lines;
A growth database by tree for storing growth data by tree, which is growth data divided by tree type;
A tree database for storing tree data relating to trees adjacent to the distribution line;
A disaster probability database storing disaster probability data of the disaster occurring in the distribution line due to the interaction between the tree and the natural phenomenon;
A growth simulation means for performing a growth simulation of a tree adjacent to the distribution line based on at least the tree data and the tree-specific growth data;
A disaster tree prediction means for predicting a disaster tree that may cause the distribution line disaster based on the growth simulation;
A distribution line disaster probability calculating means for calculating distribution line disaster probability data generated in the distribution line based on at least the disaster tree, the data related to the natural phenomenon, and the disaster probability data; Prediction system. The distribution line disaster according to claim 1, further comprising tree data registration / change means for registering new tree data in the tree database or changing existing tree data stored in the tree database. Prediction system. The distribution line disaster prediction system according to claim 1, further comprising a distribution line disaster record database storing the past distribution line disaster probability data and past distribution line disaster record. 4. The distribution line disaster prediction system according to claim 1, wherein the natural phenomenon is snowfall, and the disaster is snow damage. 4. The distribution line disaster prediction system according to claim 1, wherein the natural phenomenon is wind, and the disaster is wind damage.

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