CN108960599B - Power transmission line rainstorm disaster refined prediction method and system based on inversion algorithm - Google Patents
Power transmission line rainstorm disaster refined prediction method and system based on inversion algorithm Download PDFInfo
- Publication number
- CN108960599B CN108960599B CN201810653331.3A CN201810653331A CN108960599B CN 108960599 B CN108960599 B CN 108960599B CN 201810653331 A CN201810653331 A CN 201810653331A CN 108960599 B CN108960599 B CN 108960599B
- Authority
- CN
- China
- Prior art keywords
- transmission line
- power transmission
- rainstorm
- disaster
- rainstorm disaster
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 230000005540 biological transmission Effects 0.000 title claims abstract description 125
- 238000000034 method Methods 0.000 title claims abstract description 43
- 238000004364 calculation method Methods 0.000 claims abstract description 30
- 239000003086 colorant Substances 0.000 claims abstract description 7
- 239000002689 soil Substances 0.000 claims description 13
- 238000013507 mapping Methods 0.000 claims description 6
- 238000005259 measurement Methods 0.000 claims description 6
- 239000004575 stone Substances 0.000 claims description 6
- 238000001914 filtration Methods 0.000 claims description 4
- 238000012163 sequencing technique Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 abstract 1
- 230000000694 effects Effects 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 2
- 238000011156 evaluation Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000001556 precipitation Methods 0.000 description 2
- 238000005070 sampling Methods 0.000 description 2
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 2
- 238000010276 construction Methods 0.000 description 1
- 230000010485 coping Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000007670 refining Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000013058 risk prediction model Methods 0.000 description 1
- 238000012216 screening Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Human Resources & Organizations (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Strategic Management (AREA)
- Entrepreneurship & Innovation (AREA)
- Health & Medical Sciences (AREA)
- Theoretical Computer Science (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Physics & Mathematics (AREA)
- Public Health (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Educational Administration (AREA)
- General Health & Medical Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Alarm Systems (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The invention relates to the field of meteorological disaster early warning of an electric power system, and discloses a power transmission line rainstorm disaster refined prediction method and system based on an inversion algorithm, so that a rainstorm disaster prediction result is refined to a specific power transmission line. The method comprises the following steps: generating a rainfall grid point forecast file comprising the rainstorm disaster index predicted value of each grid point through a rainstorm disaster prediction calculation model, and performing space intersection calculation on coordinate information of the power transmission line tower by utilizing space search of a power grid GIS (geographic information system) to obtain power transmission line tower information of a rainstorm area; further, power transmission line spatial information of the rainstorm disaster is formed by utilizing the power grid GIS spatial line; and generating a power transmission line rainstorm disaster prediction early warning distribution diagram by utilizing a meteorological element space interpolation method and combining the power transmission line affected by the rainstorm disaster through the rainstorm disaster prediction grid point file, so as to draw the power transmission line under the same rainstorm disaster index in the same color and distinguish the power transmission line under different rainfall indexes in other colors.
Description
Technical Field
The invention relates to the field of meteorological disaster early warning of an electric power system, in particular to a power transmission line rainstorm disaster fine prediction method and system based on an inversion algorithm.
Background
In recent years, with the frequent occurrence of heavy rainfall weather in summer, the frequent occurrence of geological disasters such as collapse around a power transmission line, landslide, debris flow, ground cracks, water and soil loss and the like is easy to cause great harm to the safe operation of a power grid, and great economic loss and social influence are caused. Considering that the power industry is the high-weather-sensitivity and high-demand industry, once meteorological elements (including temperature, humidity, wind speed and direction, precipitation and the like) are changed, meteorological disasters and secondary disasters such as rainstorms, strong winds and the like can be caused, and the stable operation of a power transmission line is threatened. Therefore, the development of the rainstorm disaster refined prediction work of the power transmission line becomes the essential work for operation and inspection of the power industry, and the construction of the rainstorm disaster refined prediction system of the power transmission line has important significance and engineering practical value for constructing a strong power grid.
At present, a plurality of domestic units have developed related researches on prediction and early warning of rainstorm of power transmission lines, wherein an analysis model is established and early warning is carried out in time aiming at a power grid GIS (geographic information system) -based geological disaster early warning method and device for analyzing data such as geological conditions, meteorological elements and the like, so that accurate prediction of geological disasters in a power grid coverage area is realized, and the typical representative is CN 104952212A; patents such as CN102930348A and the like establish a method for evaluating the rainstorm disaster risk of the foundation slopes of all towers in the power transmission line of the section on the whole section slope based on a hierarchical structure model, so as to realize accurate evaluation of the rainstorm disaster risk of all sections of the power transmission line; the patents such as CN106022953A accurately calculate the disaster factor degree, the pregnant disaster environment sensitivity, the disaster-bearing body vulnerability and the disaster prevention and reduction capability of the power grid disaster causing the rainstorm flood through disaster situation data, rainstorm data, social and economic data, basic geographic information data and power grid distribution data, and evaluate the rainstorm risk. However, the methods described above do not mention a calculation method for the influence of the rainstorm disaster risk on the line and a prediction method for refining the line to a specific degree, which may result in untimely rainstorm early warning time, low precision and unsatisfactory effect.
Aiming at the problems of the method, a power transmission line rainstorm disaster refined prediction method with stronger initiative and high precision is urgently needed, and the capability of the power transmission line for coping with the rainstorm disaster and the safe and stable operation level are improved.
Disclosure of Invention
The invention aims to disclose a power transmission line rainstorm disaster refined prediction method and system based on an inversion algorithm, so that a rainstorm disaster prediction result is refined to a specific power transmission line.
In order to achieve the purpose, the invention discloses a power transmission line rainstorm disaster refined prediction method based on an inversion algorithm, which comprises the following steps:
step S1, generating rainfall grid point forecast files through a rainstorm disaster forecasting calculation model according to meteorological feature element data, topographic and topographic feature data, basic features of all levels of towers of the power transmission line, soil compactness, stratum lithology, crushed stone content in soil, slope characteristic characteristics, debris flow disaster factor data and historical rainstorm disaster data under the selected date, wherein the rainfall grid point forecast files comprise rainstorm disaster index forecasting values of all grid points;
step S2, reading the rainfall grid point forecast file, and acquiring grid information related to all rainfall;
step S3, mapping all rainfall related grids to isosurface areas displaying different rainstorm disaster indexes in a power grid GIS;
step S4, performing space intersection calculation on the coordinate information of the transmission line tower by using the space search of the power grid GIS, and acquiring the transmission line tower information in the rainstorm area;
step S5, the numbers, line names, voltage levels and rainstorm disaster indexes of the front and rear power transmission line towers are recorded into a file by sequencing the information of the power transmission line towers obtained in the step S4, and the recorded file is stored in a power transmission line rainfall index database;
step S6, connecting the coordinates of the line tower in the rainfall index database of the transmission line by using a power grid GIS space line to form the space information of the transmission line in a rainstorm disaster;
step S7, the power transmission line under the same rainstorm disaster index is depicted in the same color, the power transmission line under different rainfall indexes is distinguished by other colors, and the power transmission line is output to a power grid GIS interface to be displayed;
step S8, generating a power transmission line rainstorm disaster prediction early warning distribution map by using a meteorological element space interpolation method and combining the power transmission line affected by the rainstorm disaster in the step S7 through the rainstorm disaster prediction grid point file obtained in the step S1;
the meteorological element space interpolation method comprises the following calculation formula:
wherein Z is an interpolation point estimate, ZiIs the measured sample value; n is the number of actual measurement samples involved in the calculation, DiFor the distance of the interpolation point from the ith sample point, the weight index p is the power of the distance, Di -pIs a distance decay function.
Corresponding to the method, the invention also discloses a power transmission line rainstorm disaster refined prediction system based on an inversion algorithm, which comprises the following steps:
the data receiving module is used for accessing meteorological feature element data, topographic and geomorphic data, basic features of base towers of the power transmission line, soil compactness, stratum lithology, broken stone content in soil, slope characteristic characteristics, debris flow disaster factor data and historical rainstorm disaster data to a power grid GIS rainstorm disaster system database;
the rainfall capacity grid point forecasting file comprises rainfall capacity grid point index forecasting values of all grid points; reading a rainfall grid point forecast file, and acquiring all rainfall related grid information; mapping all rainfall related grids to isosurface areas displaying different rainstorm disaster indexes in the power grid GIS; carrying out space intersection calculation on the coordinate information of the transmission line tower by utilizing space search of a power grid GIS (geographic information System), and acquiring the transmission line tower information of a rainstorm area; the obtained information of the transmission towers is sequenced, the serial numbers, the line names, the voltage levels and the rainstorm disaster indexes of the transmission towers before and after are recorded into a file, and the file is stored in a transmission line rainfall index database; connecting the coordinates of the line towers in the rainfall index database of the power transmission line by using a power grid GIS space line to form power transmission line space information of the rainstorm disaster;
the image layer display module is used for displaying the isosurface areas of different rainstorm disaster indexes and the power transmission line rainstorm disaster prediction and early warning distribution map, describing the power transmission lines under the same rainstorm disaster indexes in the power transmission line rainstorm disaster prediction and early warning distribution map in the same color, and distinguishing the power transmission lines under different rainfall indexes in other colors;
the rainstorm disaster influence index line inversion calculation module is used for generating a rainstorm disaster prediction early warning distribution map of the power transmission line by using a meteorological element space interpolation method and combining the power transmission line influenced by the rainstorm disaster through a rainstorm disaster prediction grid point file;
the meteorological element space interpolation method comprises the following calculation formula:
wherein Z is an interpolation point estimate, ZiIs the measured sample value; n is the number of actual measurement samples involved in the calculation, DiFor the distance of the interpolation point from the ith sample point, the weight index p is the power of the distance, Di -pIs a distance decay function.
The invention has the following beneficial effects:
simple, practical and strong in operability. By utilizing an inversion algorithm based on a meteorological element space interpolation method and combining with a power grid GIS, the rainstorm disaster influence area in the national range can be checked, and rainstorm disaster risk influence lines in a typical area can be acquired. Not only considered the influence on the rainstorm disaster risk influence area, but also elaborated the line with specific influence, thereby realizing the rainstorm disaster risk prediction of the power transmission line.
Detailed Description
The following detailed description of embodiments of the invention, but the invention can be practiced in many different ways, as defined and covered by the claims.
Example 1
The embodiment discloses a power transmission line rainstorm disaster refined prediction method based on an inversion algorithm, which comprises the following steps:
and step S1, determining a rainstorm disaster prediction calculation model, and generating a rainfall grid point forecast file through the rainstorm disaster prediction calculation model, wherein the rainfall grid point forecast file comprises the rainstorm disaster index prediction value of each grid point.
In the above steps, optionally, the rainstorm disaster risk prediction model may adopt a rainstorm disaster risk prediction method as described in patent CN102930348A or CN 104952212A; the inputs corresponding to the model may be: according to meteorological feature element data, topographic and geomorphic data, basic features of towers at all levels of the power transmission line, soil compactness, stratum lithology, broken stone content in soil, slope characteristic characteristics, debris flow disaster factor data, historical rainstorm disaster data and the like on the selected date.
Further, the meteorological feature element data may include: minimum temperature, maximum temperature, average temperature, precipitation, humidity, maximum wind speed, average wind speed, maximum wind speed and the like; historical rainstorm disaster data may include: time, longitude, latitude, period, influence line name, etc.; the terrain vegetation data may include: data such as water system, residential area, railway, highway, border, terrain, auxiliary elements, coordinate network, vegetation type, boundary range and data quality; the foundation characteristics of each base tower of the power transmission line can comprise an independent foundation, a pile foundation, a digging foundation and the like; the debris flow disaster factor data can comprise mud level, mud speed, infrasound, earth sound and the like.
On the other hand, the format of the rainstorm grid forecast file can be a weather MICAPS fourth data format, and is mainly used for outputting the grid rainstorm disaster index contour line.
And step S2, reading the rainfall grid point forecast file and acquiring grid information related to all rainfall.
And step S3, mapping all rainfall related grids to isosurface areas displaying different storm disaster indexes in the power grid GIS. Namely: and in the equivalent surfaces of different rainstorm indexes displayed in the power grid GIS, the rainstorm disaster risk indexes in the same area are equal.
And step S4, performing space intersection calculation on the coordinate information of the transmission line tower by using the space search of the power grid GIS, and acquiring the transmission line tower information in the rainstorm area. The space intersection operation is used for returning the intersection of the two geometries; the intersection is always returned as a set, which is the smallest dimension of the source geometry.
And step S5, recording the serial numbers, the line names, the voltage levels and the rainstorm disaster indexes of the front and rear power transmission line towers into files by sequencing the power transmission line tower information acquired in the step S4, and storing the files into a power transmission line rainfall index database. The number, the line name and the voltage grade of the power transmission line towers are main judgment logics for screening whether an actual space connecting line exists between the two towers or not so as to realize the early warning analysis of the rainstorm disaster risk index of the power transmission line under different voltage grades.
And step S6, connecting the coordinates of the line tower in the rainfall index database of the power transmission line by using a power grid GIS space line to form power transmission line space information of a rainstorm disaster.
And step S7, the power transmission lines under the same rainstorm disaster index are depicted in the same color, the power transmission lines under different rainfall indexes are distinguished by other colors, and the power transmission lines are output to a power grid GIS interface to be displayed.
And step S8, generating a power transmission line rainstorm disaster prediction early warning distribution map by combining the power transmission line affected by the rainstorm disaster in the step S7 through the rainstorm disaster prediction grid point file obtained in the step S1 by using a meteorological element space interpolation method.
In this step, the meteorological element spatial interpolation method specifically adopts an inverse distance weight interpolation method. In order to reflect the change rule of meteorological elements in space and time, discrete station data is required to be used for carrying out space interpolation and converting the data into a continuous data curved surface. The principle of the inverse distance weight interpolation method is based on the first law of geography, namely, similar similarity, that is, the closer to an estimated grid point, the greater the influence of the sample point on the grid point, the smaller the influence of the sample point farther away, and when the sampling point is beyond a certain range from the interpolation point, the influence can be ignored. The value at any interpolation point is the sum of the weights of the sampling points and can be expressed as follows:
wherein Z is an interpolation point estimate, ZiIs the measured sample value; n is the number of actual measurement samples involved in the calculation, DiFor the distance of the interpolation point from the ith sample point, the weight index p is the power of the distance, Di -pIs a distance decay function.
The larger p, the faster the effect of distance decays, and the lower the weight assigned to the sample point. When p is 0, the distance has no effect; when p is 1, the effect of distance is linear; when p is>When p is 2, the method is called inverse square weighting. The weight index p significantly affects the interpolation result, the higher the power of the distance, the smoother the interpolation result, and the selection criterion is the minimum mean absolute error, which is usually 1 or 2, and the embodiment preferably takes 2. In general, Mean Error (ME), Mean Absolute Error (MAE), Root Mean Square Error (RMSE) and coefficient of determination (R) are used2) And (5) carrying out inspection and prediction precision evaluation on the model. The calculation formulas are respectively as follows:
in the above formulae XfAnd X0Respectively representing the interpolation and the measured value of a certain point;
represents the average value of the measured values; n is the number of sites used for authentication.
Preferably, the method of this embodiment further includes: setting a display threshold value in the generated electric transmission line rainstorm disaster prediction early warning distribution map; and filtering the power transmission line which is smaller than the threshold value after the meteorological element space interpolation method is solved so as not to perform color matching display on a power grid GIS interface.
Example 2
Corresponding to the above method, the embodiment discloses a power transmission line rainstorm disaster refined prediction system based on an inversion algorithm, which includes:
the data receiving module is used for accessing meteorological feature element data, topographic and geomorphic data, basic features of base towers of the power transmission line, soil compactness, stratum lithology, broken stone content in soil, slope characteristic characteristics, debris flow disaster factor data and historical rainstorm disaster data to a power grid GIS rainstorm disaster system database;
the rainfall capacity grid point forecasting file comprises rainfall capacity grid point index forecasting values of all grid points; reading a rainfall grid point forecast file, and acquiring all rainfall related grid information; mapping all rainfall related grids to isosurface areas displaying different rainstorm disaster indexes in the power grid GIS; carrying out space intersection calculation on the coordinate information of the transmission line tower by utilizing space search of a power grid GIS (geographic information System), and acquiring the transmission line tower information of a rainstorm area; the obtained information of the transmission towers is sequenced, the serial numbers, the line names, the voltage levels and the rainstorm disaster indexes of the transmission towers before and after are recorded into a file, and the file is stored in a transmission line rainfall index database; connecting the coordinates of the line towers in the rainfall index database of the power transmission line by using a power grid GIS space line to form power transmission line space information of the rainstorm disaster;
the image layer display module is used for displaying the isosurface areas of different rainstorm disaster indexes and the power transmission line rainstorm disaster prediction and early warning distribution map, describing the power transmission lines under the same rainstorm disaster indexes in the power transmission line rainstorm disaster prediction and early warning distribution map in the same color, and distinguishing the power transmission lines under different rainfall indexes in other colors;
the rainstorm disaster influence index line inversion calculation module is used for generating a rainstorm disaster prediction early warning distribution map of the power transmission line by using a meteorological element space interpolation method and combining the power transmission line influenced by the rainstorm disaster through a rainstorm disaster prediction grid point file;
the meteorological element space interpolation method comprises the following calculation formula:
wherein Z is an interpolation point estimate, ZiIs the measured sample value; n is the number of actual measurement samples involved in the calculation, DiFor the distance of the interpolation point from the ith sample point, the weight index p is the power of the distance, Di -pIs a distance decay function.
Further, the layer display module of this embodiment is further configured to: setting a display threshold value in the generated electric transmission line rainstorm disaster prediction early warning distribution map; and filtering the power transmission line which is smaller than the threshold value after the meteorological element space interpolation method is solved so as not to perform color matching display on a power grid GIS interface.
In summary, the power transmission line rainstorm disaster refined prediction method and system based on the inversion algorithm disclosed in the embodiments of the present invention have the following beneficial effects:
simple, practical and strong in operability. By utilizing an inversion algorithm based on a meteorological element space interpolation method and combining with a power grid GIS, the rainstorm disaster influence area in the national range can be checked, and rainstorm disaster risk influence lines in a typical area can be acquired. Not only considered the influence on the rainstorm disaster risk influence area, but also elaborated the line with specific influence, thereby realizing the rainstorm disaster risk prediction of the power transmission line.
The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention, and various modifications and changes may be made by those skilled in the art. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. A power transmission line rainstorm disaster refined prediction method based on an inversion algorithm is characterized by comprising the following steps:
step S1, generating a rainfall grid point forecast file through a rainstorm disaster forecasting calculation model according to meteorological feature element data, topographic and topographic feature data, basic features of all levels of towers of the power transmission line, soil compactness, stratum lithology, crushed stone content in soil, slope characteristic characteristics, debris flow disaster factor data and historical rainstorm disaster data under the selected date, wherein the rainfall grid point forecast file comprises rainstorm disaster index forecasting values of all grid points;
step S2, reading the rainfall grid point forecast file, and acquiring grid point information related to all rainfall;
step S3, mapping all rainfall related grid points to isosurface areas displaying different rainstorm disaster indexes in a power grid GIS;
step S4, performing space intersection calculation on the coordinate information of the transmission line tower by using the space search of the power grid GIS, and acquiring the transmission line tower information in the rainstorm area;
step S5, the serial numbers, the line names, the voltage levels and the rainstorm disaster indexes of the sequenced transmission line towers are recorded into a file by sequencing the transmission line tower information obtained in the step S4, and the serial numbers, the line names, the voltage levels and the rainstorm disaster indexes are stored into a transmission line rainfall index database;
step S6, connecting the coordinates of the line tower in the rainfall index database of the transmission line by using a power grid GIS space line to form the space information of the transmission line in a rainstorm disaster;
step S7, the power transmission lines under the same rainstorm disaster index are depicted in the same color, the power transmission lines under different rainstorm disaster indexes are distinguished in different colors, and the power transmission lines are output to a power grid GIS interface to be displayed;
step S8, generating a power transmission line rainstorm disaster prediction and early warning distribution map by using a meteorological element space interpolation method and combining the power transmission line under the influence of the rainstorm disaster index in the step S7 through the rainfall grid point forecast file obtained in the step S1;
the meteorological element space interpolation method comprises the following calculation formula:
wherein Z is an interpolation point estimate, ZiIs the measured sample value; n is the number of actual measurement samples involved in the calculation, DiFor the distance of the interpolation point from the ith sample point, the weight index p is the power of the distance, Di -pIs a distance decay function.
2. The method of claim 1, further comprising:
setting a display threshold value in the generated electric transmission line rainstorm disaster prediction early warning distribution map; and
and filtering the power transmission line which is smaller than the threshold value after the meteorological element space interpolation method is solved so as not to perform color matching display on a power grid GIS interface.
3. A power transmission line rainstorm disaster refined prediction system based on an inversion algorithm is characterized by comprising the following steps:
the data receiving module is used for accessing meteorological feature element data, topographic and geomorphic data, basic features of base towers of the power transmission line, soil compactness, stratum lithology, broken stone content in soil, slope characteristic characteristics, debris flow disaster factor data and historical rainstorm disaster data to a power grid GIS rainstorm disaster system database;
the rainfall disaster influence area prediction calculation module is used for generating rainfall grid point prediction files by utilizing a rainfall disaster prediction calculation model, and the rainfall grid point prediction files comprise the rainfall disaster index prediction values of all grid points; reading a rainfall grid point forecast file, and acquiring all rainfall related grid point information; mapping all rainfall related grid points to isosurface areas displaying different rainstorm disaster indexes in the power grid GIS; carrying out space intersection calculation on the coordinate information of the transmission line tower by utilizing space search of a power grid GIS (geographic information System), and acquiring the transmission line tower information of a rainstorm area; the obtained information of the transmission towers is sequenced, and the serial numbers, the line names, the voltage levels and the rainstorm disaster indexes of the sequenced transmission towers are recorded into a file and stored into a transmission line rainfall index database; connecting the coordinates of the line towers in the rainfall index database of the power transmission line by using a power grid GIS space line to form power transmission line space information of the rainstorm disaster;
the image layer display module is used for displaying the isosurface areas of different rainstorm disaster indexes and the power transmission line rainstorm disaster prediction and early warning distribution map, describing the power transmission lines under the same rainstorm disaster indexes in the power transmission line rainstorm disaster prediction and early warning distribution map in the same color, and distinguishing the power transmission lines under the different rainstorm disaster indexes in different colors;
the rainstorm disaster influence index line inversion calculation module is used for predicting grid point files through rainstorm disasters, generating a rainstorm disaster prediction early warning distribution map of the power transmission line by utilizing a meteorological element space interpolation method and combining power transmission lines under different rainstorm disaster indexes;
the meteorological element space interpolation method comprises the following calculation formula:
wherein Z is an interpolation point estimate, ZiIs the measured sample value; n is the number of actual measurement samples involved in the calculation, DiThe distance between the interpolation point and the ith sample point is defined as the weightThe number p being a power of the distance, Di -pIs a distance decay function.
4. The inversion algorithm based power transmission line rainstorm disaster refined prediction system of claim 3, wherein the map layer display module is further configured to:
setting a display threshold value in the generated electric transmission line rainstorm disaster prediction early warning distribution map; and
and filtering the power transmission line which is smaller than the threshold value after the meteorological element space interpolation method is solved so as not to perform color matching display on a power grid GIS interface.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810653331.3A CN108960599B (en) | 2018-06-22 | 2018-06-22 | Power transmission line rainstorm disaster refined prediction method and system based on inversion algorithm |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810653331.3A CN108960599B (en) | 2018-06-22 | 2018-06-22 | Power transmission line rainstorm disaster refined prediction method and system based on inversion algorithm |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN108960599A CN108960599A (en) | 2018-12-07 |
| CN108960599B true CN108960599B (en) | 2020-04-07 |
Family
ID=64486190
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810653331.3A Active CN108960599B (en) | 2018-06-22 | 2018-06-22 | Power transmission line rainstorm disaster refined prediction method and system based on inversion algorithm |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108960599B (en) |
Families Citing this family (15)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109784683A (en) * | 2018-12-27 | 2019-05-21 | 国网湖南省电力有限公司 | A kind of power grid wide area meteorological disaster integrated campaign method |
| CN110502571B (en) * | 2019-08-29 | 2020-05-08 | 智洋创新科技股份有限公司 | Method for identifying visible alarm high-power-generation line segment of power transmission line channel |
| CN110632681B (en) * | 2019-09-17 | 2022-05-27 | 国网湖南省电力有限公司 | Machine learning-based short-term and imminent early warning method and system for afternoon thunderstorm of power grid |
| CN110633858A (en) * | 2019-09-18 | 2019-12-31 | 国网湖南省电力有限公司 | Clustering early warning method and system for rainstorm geological disasters of power transmission line |
| CN112818073B (en) * | 2019-11-15 | 2022-12-09 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | Method for determining meteorological icing position of power transmission line |
| CN111126846A (en) * | 2019-12-24 | 2020-05-08 | 广东电网有限责任公司 | Method for evaluating differentiation state of overhead transmission line |
| CN111639865B (en) * | 2020-06-02 | 2021-03-19 | 中国气象局气象探测中心 | High-speed rail line meteorological disaster occurrence risk analysis method |
| CN112132795B (en) * | 2020-09-14 | 2023-02-03 | 中山大学 | Electric tower disaster risk assessment method and system based on LiDAR point cloud |
| CN112308395B (en) * | 2020-10-27 | 2022-06-14 | 国网江西省电力有限公司电力科学研究院 | Screening method and device for important power transmission channel |
| CN112381327A (en) * | 2020-12-01 | 2021-02-19 | 国网湖南省电力有限公司 | Power transmission channel gale disaster forecasting method and system |
| CN113469268B (en) * | 2021-07-16 | 2023-03-31 | 云南电网有限责任公司电力科学研究院 | Error statistical analysis-based rainfall correction method and device for power transmission line tower |
| CN113570133B (en) * | 2021-07-26 | 2024-05-24 | 广西电网有限责任公司电力科学研究院 | Power transmission and distribution line risk prediction method and system for coping with heavy rainfall |
| CN114358405B (en) * | 2021-12-27 | 2023-01-31 | 中国电建集团贵州电力设计研究院有限公司 | Refined point-to-point temperature prediction method for power transmission line |
| CN114896786A (en) * | 2022-05-09 | 2022-08-12 | 广东省韶关市气象局 | Expressway rainstorm early warning method and system based on numerical mode and live data |
| CN118091799A (en) * | 2024-04-26 | 2024-05-28 | 大连智水慧成科技有限责任公司 | Multi-mode integrated forecasting method, system, device, storage medium and program product |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930348A (en) * | 2012-10-19 | 2013-02-13 | 广东电网公司电力科学研究院 | Assessment method for rainstorm disaster risks of sectional power transmission line pole-tower foundation slopes |
| CN104851051A (en) * | 2014-12-08 | 2015-08-19 | 国家电网公司 | Dynamic-modification-combined storm rainfall fine alarming method for power grid zone |
| CN107169645A (en) * | 2017-05-09 | 2017-09-15 | 云南电力调度控制中心 | A kind of transmission line malfunction probability online evaluation method of meter and Rainfall Disaster influence |
| CN107958312A (en) * | 2017-12-12 | 2018-04-24 | 国网湖南省电力有限公司 | Transmission line galloping Forecasting Methodology, system and storage medium based on inversion algorithm |
-
2018
- 2018-06-22 CN CN201810653331.3A patent/CN108960599B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930348A (en) * | 2012-10-19 | 2013-02-13 | 广东电网公司电力科学研究院 | Assessment method for rainstorm disaster risks of sectional power transmission line pole-tower foundation slopes |
| CN104851051A (en) * | 2014-12-08 | 2015-08-19 | 国家电网公司 | Dynamic-modification-combined storm rainfall fine alarming method for power grid zone |
| CN107169645A (en) * | 2017-05-09 | 2017-09-15 | 云南电力调度控制中心 | A kind of transmission line malfunction probability online evaluation method of meter and Rainfall Disaster influence |
| CN107958312A (en) * | 2017-12-12 | 2018-04-24 | 国网湖南省电力有限公司 | Transmission line galloping Forecasting Methodology, system and storage medium based on inversion algorithm |
Also Published As
| Publication number | Publication date |
|---|---|
| CN108960599A (en) | 2018-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108960599B (en) | Power transmission line rainstorm disaster refined prediction method and system based on inversion algorithm | |
| CN111582755B (en) | Mountain torrent disaster comprehensive risk dynamic assessment method based on multi-dimensional set information | |
| CN109584510B (en) | Road high slope landslide hazard early warning method based on evaluation function training | |
| CN111651885A (en) | Intelligent sponge urban flood forecasting method | |
| Xu et al. | Integrated hydrologic modeling and GIS in water resources management | |
| Ahmad et al. | Estimation of a unique pair of Nash model parameters: an optimization approach | |
| CN113409550B (en) | Debris flow disaster early warning method and system based on runoff convergence simulation | |
| CN113470333A (en) | Risk assessment and monitoring early warning system for shallow landslide of line engineering corridor | |
| CN113283802A (en) | Landslide risk assessment method for complex and difficult mountain area | |
| CN113987813A (en) | Landslide sensitivity mapping model based on multi-parameter decision and analytic hierarchy process | |
| Hong et al. | Effects of land cover changes induced by large physical disturbances on hydrological responses in Central Taiwan | |
| Mahmood Siddiqui et al. | Flood inundation modeling for a watershed in the pothowar region of Pakistan | |
| Madi et al. | Flood Vulnerability Mapping and Risk Assessment Using Hydraulic Modeling and GIS in Tamanrasset Valley Watershed, Algeria | |
| Purwandari | A GIS modelling approach for flood hazard assessment in part of Surakarta city, Indonesia | |
| Abd Rahman et al. | Digital surface model (DSM) construction and flood hazard simulation for Development Plans in Naga City, Philippines | |
| Tate et al. | An innovative flood forecasting system for the Demer basin: A case study | |
| Chandrasekar et al. | Computer application on evaluating beach sediment erosion and accretion from profile survey data | |
| Rebêlo et al. | Monitoring the Cresmina dune evolution (Portugal) using differential GPS | |
| Rowland et al. | Floodplain mapping and risks assessment of the Orashi River using remote sensing and GIS in the Niger Delta Region, Nigeria | |
| Singh et al. | Mapping and prediction of surface run-off using SCS-CN method | |
| Divín et al. | Effects of land use changes on the runoff in the landscape based on hydrological simulation in HEC-HMS and HEC-RAS using different elevation data. | |
| Shaviraachin | Flood simulation: a case study in the lower Limpopo valley, Mozambique using the SOBEK flood model | |
| Abiri | Assessment of Flood Risks in Ifo Local Government Area of Ogun State | |
| Hu | Combining Continuous and Event-based Hydrologcial Modeling in Kävlinge river Basin with HEC-HMS | |
| Kebir et al. | Flood Risk Assessment Using Hydraulic Modeling and Remote Sensing Data: A Case Study of the M’zab Valley,(Algeria) |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |