CN116127855A - Power grid icing disaster risk judging method and related equipment - Google Patents

Power grid icing disaster risk judging method and related equipment Download PDF

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CN116127855A
CN116127855A CN202310275723.1A CN202310275723A CN116127855A CN 116127855 A CN116127855 A CN 116127855A CN 202310275723 A CN202310275723 A CN 202310275723A CN 116127855 A CN116127855 A CN 116127855A
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icing
grid
disaster
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马御棠
耿浩
周仿荣
徐真
高振宇
文刚
马仪
曹俊
潘浩
王国芳
顾仕强
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Electric Power Research Institute of Yunnan Power Grid Co Ltd
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Abstract

The embodiment of the invention discloses a method for judging risk of ice coating disaster of a power grid and related equipment, wherein the method comprises the following steps: acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid racks in the target area; acquiring an icing disaster risk level of the grid rack in the target area based on the real-time icing information, the weather forecast data and a preset icing prediction model, wherein the preset icing prediction model comprises a BP neural network; according to the icing disaster risk level, a space analysis method is adopted to obtain a power grid equipment list affected by the icing disaster in the target area; and acquiring disaster affected conditions and grid risk point distribution data of the power grid in the target area by adopting a statistical analysis method based on a power grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data.

Description

Power grid icing disaster risk judging method and related equipment
Technical Field
The invention relates to the technical field of power transmission, in particular to a power grid icing disaster risk judging method and related equipment.
Background
The regions of Yunnan such as Qujing, zhaotong and Kunming have higher altitudes, are in quasi-stationary front active regions, and have very frequent cold air activities. In recent years, the damage tripping and power failure of partial lines of a power grid are affected by continuous low-temperature rain, snow and freezing severe weather. At present, an icing sensing system mainly comprising on-line monitoring and auxiliary manual ice observation is established in the aspect of a main network, and an icing observation system of 'ice observation station + automatic ice observation point' of a company is formed, but due to low coverage rate of a monitoring terminal and less power operation and maintenance personnel, the prevention and maintenance of icing disasters are lack of pertinence, and further the stable operation of a power grid is affected.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method and related device for determining risk of icing disaster of a power grid, which are used for solving the problem that in the prior art, pertinence is lacking in preventing and maintaining icing disaster. In order to achieve one or a part or all of the above objects or other objects, the present application provides a method for determining risk of ice coating disaster of a power grid, including:
acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid racks in the target area;
substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain an icing disaster risk level of the grid rack in the target area, wherein the preset icing prediction model comprises a BP neural network;
according to the icing disaster risk level, a space analysis method is adopted to obtain a power grid equipment list affected by the icing disaster in the target area;
and acquiring disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data.
Optionally, the step of acquiring weather forecast data of the target area includes:
acquiring initial weather forecast data of the target area;
and carrying out refinement treatment on the initial weather forecast data by adopting an IDW interpolation method to obtain target weather forecast data in a preset format.
Optionally, the step of acquiring real-time ice coating information of the grid rack in the target area includes:
generating ice coating information of the target area according to the target weather forecast data in the preset format;
and acquiring real-time ice coating information of the grid rack in the target area based on the ice coating information of the target area.
Optionally, before the step of substituting the real-time icing information and the weather forecast data into a preset icing prediction model to obtain the icing disaster risk level of the grid rack in the target area, the method further includes:
acquiring historical ice covering information of a grid rack in the target area based on longitude and latitude of the target area;
and training the initial icing prediction model based on the historical icing information to obtain the preset icing prediction model.
Optionally, the step of substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain an icing disaster risk level of the grid rack in the target area includes:
substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model containing a BP neural network to obtain predicted icing data of the grid rack in the target area;
and matching the predicted icing data with thresholds of different icing disaster risk grades to obtain the target icing disaster risk grade of the grid rack in the target area.
Optionally, the step of obtaining the grid equipment list affected by the icing disaster in the target area by adopting a spatial analysis method according to the icing disaster risk level includes:
acquiring the influence state of the target icing disaster risk level on power grid equipment on a power grid rack in the target area by adopting a buffer area analysis method of Oracle Spatial space analysis;
and obtaining a power grid equipment list affected by the ice coating disaster in the target area based on the influence state.
Optionally, the step of obtaining disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on the grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data includes:
counting the disaster affected condition of the whole grid rack in the target area according to the grid equipment list;
obtaining damage risk of the power grid equipment based on factory data of the power grid equipment and a target icing disaster risk level of a preset area where the power grid equipment is located;
generating grid risk point distribution data based on the damage risk of the power grid equipment;
and generating an early warning report according to the disaster affected situation and the grid risk point distribution data.
On the other hand, the application provides a power grid icing disaster risk judging device, including:
the data acquisition module is used for acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid rack in the target area;
the prediction module is used for substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain the icing disaster risk level of the grid rack in the target area, wherein the preset icing prediction model comprises a BP neural network;
the analysis module is used for obtaining a power grid equipment list affected by the icing disaster in the target area by adopting a space analysis method according to the icing disaster risk level;
the early warning module is used for acquiring disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data.
In another aspect, an embodiment of the present application provides an electronic device, including: the system comprises a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, the processor and the memory are communicated through the bus when the electronic device runs, and the machine-readable instructions are executed by the processor to execute the steps of the grid icing disaster risk judging method.
In another aspect, an embodiment of the present application provides a computer readable storage medium, where a computer program is stored, where the computer program is executed by a processor to perform the steps of the method for determining risk of icing disaster of a power grid as described above.
The implementation of the embodiment of the invention has the following beneficial effects:
acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid rack in the target area; substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain an icing disaster risk level of the grid rack in the target area, wherein the preset icing prediction model comprises a BP neural network; according to the icing disaster risk level, a space analysis method is adopted to obtain a power grid equipment list affected by the icing disaster in the target area; and acquiring disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data. And predicting the icing disaster risk level of the grid rack in the target area, correlating the prediction result with the GIS coordinates of the grid tower of the Yunnan grid power transmission line to obtain a power transmission line icing prediction result of the grid tower level, further predicting a grid equipment list affected by the icing disaster, being beneficial to supporting anti-icing and anti-icing work, realizing full-time tracking of line icing through icing monitoring and icing prediction, finely predicting future power transmission line icing, supporting a production unit to reasonably set ice observation personnel during a cold tide, timely controlling icing dynamics of the power transmission line, and guaranteeing safe and stable operation of the power grid in the icing period.
Drawings
In order to more clearly illustrate the embodiments of the invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Wherein:
fig. 1 is a flowchart of a method for determining risk of ice coating disaster of a power grid according to an embodiment of the present application;
fig. 2 is a flowchart of another method for determining risk of ice coating disaster of a power grid according to an embodiment of the present application;
fig. 3 is a schematic structural diagram of an ice covering disaster risk judging device for a power grid according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present application;
fig. 5 is a schematic structural diagram of a storage medium according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The application provides a power grid icing disaster risk judging method, as shown in fig. 1, comprising the following steps:
s101, acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid rack in the target area;
for example, future 3 weather forecast data including data such as temperature A1, humidity A2, rainfall A3, wind speed A5, wind direction A6 and the like are obtained from an external weather table, grid frame data including a tower longitude C1, a latitude C2 and tower foundation information C3 are obtained from a grid internal information management platform, the tower foundation information C3 includes a line to which a tower belongs, a horizontal span, a vertical span, a service life and the like, and real-time icing information of the grid frame in the target area, namely, on-line monitoring data including a longitude B1, a latitude B2, an icing thickness B3 and an icing time B4 is obtained.
S102, substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain the icing disaster risk level of the grid rack in the target area, wherein the preset icing prediction model comprises a BP neural network;
the icing disaster risk is predicted based on real-time icing monitoring information, future 24, 48 and 72-hour weather forecast data and grid rack information, and the BP neural network is adopted to conduct icing prediction of the power transmission line, so that the icing disaster risk level is obtained.
S103, according to the icing disaster risk level, a space analysis method is adopted to obtain a power grid equipment list affected by the icing disaster in the target area;
in a possible implementation manner, the step of obtaining a grid equipment list affected by the icing disaster in the target area by adopting a spatial analysis method according to the icing disaster risk level includes:
acquiring the influence state of the target icing disaster risk level on power grid equipment on a power grid rack in the target area by adopting a buffer area analysis method of Oracle Spatial space analysis;
and obtaining a power grid equipment list affected by the ice coating disaster in the target area based on the influence state.
The method for analyzing the buffer area analysis of the Oracle Spatial space is used for analyzing the affected power grid line tower information to obtain a power grid icing disaster risk influence list, wherein the analysis function of the buffer area analysis method of the Oracle Spatial space is as follows:
sdo_geom.sdo_WITHIN_DISTANCE (sdo_Geometry 1DISTANCE, sdo_Geometry2, tolerance, 'unit') is used to determine tower information about the perimeter of the grid ice coating hazard risk WITHIN a specified DISTANCE. Parameter description: sdo_geometry1 and sdo_geometry2 are geometric objects corresponding to the spatial data. Tolerance, an allowable precision range; distance is a specified Distance; the Unit for representing the distance is equal length Unit of unit=m/unit=km, but must be one of the UNITS listed in the sdo_dist_units table.
S104, acquiring disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data.
Illustratively, based on the grid icing disaster risk influence list, grid equipment icing disaster statistics and early warning are carried out, and a refined grid icing disaster distribution map is generated.
Acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid rack in the target area; substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain an icing disaster risk level of the grid rack in the target area, wherein the preset icing prediction model comprises a BP neural network; according to the icing disaster risk level, a space analysis method is adopted to obtain a power grid equipment list affected by the icing disaster in the target area; and acquiring disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data. And predicting the icing disaster risk level of the grid rack in the target area, correlating the prediction result with the GIS coordinates of the grid tower of the Yunnan grid power transmission line to obtain a power transmission line icing prediction result of the grid tower level, further predicting a grid equipment list affected by the icing disaster, being beneficial to supporting anti-icing and anti-icing work, realizing full-time tracking of line icing through icing monitoring and icing prediction, finely predicting future power transmission line icing, supporting a production unit to reasonably set ice observation personnel during a cold tide, timely controlling icing dynamics of the power transmission line, and guaranteeing safe and stable operation of the power grid in the icing period.
In one possible implementation manner, the step of acquiring weather forecast data of the target area includes:
acquiring initial weather forecast data of the target area;
and carrying out refinement treatment on the initial weather forecast data by adopting an IDW interpolation method to obtain target weather forecast data in a preset format.
Illustratively, the weather forecast grid data with the resolution of 7km multiplied by 7km is processed by adopting an IDW interpolation method, and finally the weather forecast grid data with the resolution of 3km multiplied by 3km is formed, so that data support is provided for icing risk prediction.
Illustratively, the IDW interpolation method performs weighted average with the distance between the interpolation point and the sample point as a weight, and the sample point closer to the interpolation point is given a greater weight. Let a series of discrete points be distributed on a plane, the coordinates and values of which are known as Xi, yi, zi (i=1, 2, …, n) are found by distance weighting. The IDW obtains an interpolation unit by averaging each sample point value of the neighboring area. The IDW interpolation method requires that the discrete points be uniformly distributed and of a density sufficient to reflect the local surface variations in the analysis
In a possible implementation manner, the step of acquiring real-time ice coating information of the grid rack in the target area includes:
generating ice coating information of the target area according to the target weather forecast data in the preset format;
and acquiring real-time ice coating information of the grid rack in the target area based on the ice coating information of the target area.
Illustratively, a target area ice field profile, i.e., a 3km×3km grid data list, is acquired, including longitude, latitude, and historical icing levels. And acquiring real-time ice coating information of the grid network frame in the target area based on the 3km multiplied by 3km grid data list.
In a possible implementation manner, before the step of substituting the real-time icing information and the weather forecast data into a preset icing prediction model to obtain the icing disaster risk level of the grid rack in the target area, the method further includes:
acquiring historical icing information of a grid rack in the target area based on the longitude and latitude of the target area;
and training the initial icing prediction model based on the historical icing information to obtain the preset icing prediction model.
Exemplary, based on meteorological element monitoring data and icing on-line monitoring data, a multisource data fusion transmission line icing prediction database is established, and data cleaning is performed. And carrying out power transmission line icing prediction model training based on a neural network algorithm to obtain a final optimized power transmission line icing prediction model.
In a possible implementation manner, the step of substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain an icing disaster risk level of the grid rack in the target area includes:
substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model containing a BP neural network to obtain predicted icing data of the grid rack in the target area;
and matching the predicted icing data with thresholds of different icing disaster risk grades to obtain the target icing disaster risk grade of the grid rack in the target area.
Illustratively, define the icing hazard risk level as: (1) predicting an ice coating thickness of 0mm and an ice coating-free area; (2) predicting an ice coating thickness of 5mm and a light ice area; (3) predicting an icing thickness of 10mm and a light ice area; (4) predicting an ice coating thickness of 15mm and a medium ice area; (5) predicting an ice coating thickness of 20mm and a heavy ice area; (6) predicting an ice coating thickness of 30mm and a heavy ice area; (7) predicting an ice coating thickness of 40mm and a heavy ice area; and (8) predicting an ice coating thickness of more than or equal to 50mm and a heavy ice area.
Substituting the real-time icing information, the weather forecast data and the grid network frame information in the target area into a preset icing prediction model comprising a BP neural network to obtain predicted icing data of the grid network frame in the target area, namely, predicted icing thickness, and obtaining a target icing disaster risk level of the grid network frame in the target area based on the predicted icing thickness and a defined icing disaster risk level threshold.
In a possible implementation manner, the step of obtaining disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on the grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data includes:
counting the disaster affected condition of the whole grid rack in the target area according to the grid equipment list;
obtaining damage risk of the power grid equipment based on factory data of the power grid equipment and a target icing disaster risk level of a preset area where the power grid equipment is located;
generating grid risk point distribution data based on the damage risk of the power grid equipment;
and generating an early warning report according to the disaster affected situation and the grid risk point distribution data.
Illustratively, according to the grid equipment list, calculating the disaster affected condition of the whole grid rack in the target area, for example, calculating whether the grid rack in the target area can continue to operate or whether part of the grid rack can continue to operate; if factory data of the power grid equipment, namely, the anti-icing thickness is A, the icing thickness corresponding to the target icing disaster risk level of the preset area where the power grid equipment is located is B, if A is greater than or equal to B, the power grid equipment has no damage risk, if A is less than B, the power grid equipment has damage risk, the damage risk of the power grid equipment is recorded to generate grid risk point distribution data, and further an early warning report is generated, so that maintenance staff can be facilitated to maintain the damaged power grid equipment in a targeted mode.
In a possible implementation manner, as shown in fig. 2, a method for determining risk of ice coating disaster of a power grid includes: step 1: acquiring future 3 weather forecast data from an external weather table, wherein the future 3 weather forecast data comprise data such as temperature A1, humidity A2, rainfall A3, wind speed A5, wind direction A6 and the like;
step 2, carrying out interpolation analysis on the meteorological data obtained in the step 1 to obtain meteorological grid data with the resolution of 3km multiplied by 3 km;
step 3, constructing an icing prediction model based on real-time icing monitoring information, historical weather and icing data, weather forecast data of 24, 48 and 72 hours in the future and grid frame information, and carrying out icing disaster risk prediction;
step 4, based on the icing disaster risk level information, a buffer area analysis method of Oracle space analysis is adopted to obtain a power grid icing disaster risk influence equipment list;
and 5, based on the grid icing disaster risk influence list, carrying out statistical analysis on disaster affected conditions and grid risk point distribution.
On the other hand, as shown in fig. 3, the present application provides a power grid icing disaster risk judging device, including:
the data acquisition module 201 is configured to acquire weather forecast data of a target area, grid rack information in the target area, and real-time icing information of the grid rack in the target area;
the prediction module 202 is configured to substitute the real-time icing information, the weather forecast data, and the grid rack information in the target area into a preset icing prediction model to obtain an icing disaster risk level of the grid rack in the target area, where the preset icing prediction model includes a BP neural network;
the analysis module 203 is configured to obtain a power grid equipment list affected by the icing disaster in the target area by adopting a spatial analysis method according to the icing disaster risk level;
the early warning module 204 is configured to obtain disaster affected conditions and grid risk point distribution data of the grid rack in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generate an early warning report according to the disaster affected conditions and the grid risk point distribution data.
In one possible implementation, as shown in fig. 4, the present application provides a computer-readable storage medium 300, on which is stored a computer program 311, which computer program 311, when executed by a processor, implements: acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid racks in the target area; substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain an icing disaster risk level of the grid rack in the target area, wherein the preset icing prediction model comprises a BP neural network; according to the icing disaster risk level, a space analysis method is adopted to obtain a power grid equipment list affected by the icing disaster in the target area; and acquiring disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data.
In one possible implementation, as shown in fig. 5, the present embodiment provides a computer-readable storage medium 400, on which is stored a computer program 411, which computer program 411, when executed by a processor, implements: acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid racks in the target area; substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain an icing disaster risk level of the grid rack in the target area, wherein the preset icing prediction model comprises a BP neural network; according to the icing disaster risk level, a space analysis method is adopted to obtain a power grid equipment list affected by the icing disaster in the target area; and acquiring disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data.
The computer storage media of embodiments of the invention may take the form of any combination of one or more computer-readable media. The computer readable medium may be a computer readable signal medium or a computer readable storage medium. The computer readable storage medium may be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.
The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.
Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider).
It will be appreciated by those of ordinary skill in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be centralized on a single computing device, or distributed over a network of computing devices, or they may alternatively be implemented in program code executable by a computer device, such that they are stored in a memory device and executed by the computing device, or they may be separately fabricated as individual integrated circuit modules, or multiple modules or steps within them may be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of numerous obvious changes, rearrangements and substitutions without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.
The foregoing disclosure is illustrative of the present invention and is not to be construed as limiting the scope of the invention, which is defined by the appended claims.

Claims (10)

1. The method for judging the risk of the ice coating disaster of the power grid is characterized by comprising the following steps of:
acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid racks in the target area;
substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain an icing disaster risk level of the grid rack in the target area, wherein the preset icing prediction model comprises a BP neural network;
according to the icing disaster risk level, a space analysis method is adopted to obtain a power grid equipment list affected by the icing disaster in the target area;
and acquiring disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data.
2. The method for determining risk of ice coating disaster of power grid according to claim 1, wherein the step of obtaining weather forecast data of the target area comprises:
acquiring initial weather forecast data of the target area;
and carrying out refinement treatment on the initial weather forecast data by adopting an IDW interpolation method to obtain target weather forecast data in a preset format.
3. The method for determining risk of ice coating disaster of power grid according to claim 1, wherein the step of obtaining real-time ice coating information of the grid rack in the target area comprises the steps of:
generating ice coating information of the target area according to the target weather forecast data in the preset format;
and acquiring real-time ice coating information of the grid rack in the target area based on the ice coating information of the target area.
4. The method for determining risk of icing disaster of power grid according to claim 1, wherein before the step of substituting the real-time icing information and the weather forecast data into a preset icing prediction model to obtain the risk level of icing disaster of power grid frame in the target area, further comprises:
acquiring historical ice covering information of a grid rack in the target area based on longitude and latitude of the target area;
and training the initial icing prediction model based on the historical icing information to obtain the preset icing prediction model.
5. The method for determining risk of ice coating disaster of power grid according to claim 1, wherein the step of substituting the real-time ice coating information, the weather forecast data and the information of the grid rack in the target area into a preset ice coating prediction model to obtain the risk level of ice coating disaster of the grid rack in the target area comprises the following steps:
substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model containing a BP neural network to obtain predicted icing data of the grid rack in the target area;
and matching the predicted icing data with thresholds of different icing disaster risk grades to obtain the target icing disaster risk grade of the grid rack in the target area.
6. The method for determining risk of ice coating disaster in power grid according to claim 5, wherein the step of obtaining the power grid equipment list affected by the ice coating disaster in the target area by adopting a spatial analysis method according to the risk level of the ice coating disaster comprises the following steps:
acquiring the influence state of the target icing disaster risk level on power grid equipment on a power grid rack in the target area by adopting a buffer area analysis method of Oracle Spatial space analysis;
and obtaining a power grid equipment list affected by the ice coating disaster in the target area based on the influence state.
7. The method for determining risk of ice coating disaster of power grid according to claim 1, wherein the step of obtaining disaster affected condition and grid risk point distribution data of the power grid rack in the target area by adopting a statistical analysis method based on the power grid equipment list affected by the ice coating disaster and generating an early warning report according to the disaster affected condition and the grid risk point distribution data comprises the following steps:
counting the disaster affected condition of the whole grid rack in the target area according to the grid equipment list;
obtaining damage risk of the power grid equipment based on factory data of the power grid equipment and a target icing disaster risk level of a preset area where the power grid equipment is located;
generating grid risk point distribution data based on the damage risk of the power grid equipment;
and generating an early warning report according to the disaster affected situation and the grid risk point distribution data.
8. The utility model provides a power grid icing disaster risk judgement device which characterized in that includes:
the data acquisition module is used for acquiring weather forecast data of a target area, grid rack information in the target area and real-time icing information of the grid rack in the target area;
the prediction module is used for substituting the real-time icing information, the weather forecast data and the grid rack information in the target area into a preset icing prediction model to obtain the icing disaster risk level of the grid rack in the target area, wherein the preset icing prediction model comprises a BP neural network;
the analysis module is used for obtaining a power grid equipment list affected by the icing disaster in the target area by adopting a space analysis method according to the icing disaster risk level;
the early warning module is used for acquiring disaster affected conditions and grid risk point distribution data of the grid network frame in the target area by adopting a statistical analysis method based on a grid equipment list affected by the ice covered disaster, and generating an early warning report according to the disaster affected conditions and the grid risk point distribution data.
9. An electronic device, comprising: a processor, a memory and a bus, the memory storing machine readable instructions executable by the processor, the processor and the memory in communication via the bus when the electronic device is running, the machine readable instructions when executed by the processor performing the steps of the grid icing hazard risk determination method of any of claims 1 to 7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium has stored thereon a computer program which, when executed by a processor, performs the steps of the grid icing hazard risk determination method according to any of claims 1 to 7.
CN202310275723.1A 2023-03-17 2023-03-17 Power grid icing disaster risk judging method and related equipment Pending CN116127855A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117172554A (en) * 2023-10-31 2023-12-05 中国铁建电气化局集团有限公司 Icing disaster risk prediction method, device, equipment and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117172554A (en) * 2023-10-31 2023-12-05 中国铁建电气化局集团有限公司 Icing disaster risk prediction method, device, equipment and storage medium

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