CN112526637A - Integrated power grid channel rainstorm monitoring method and system based on uneven weight - Google Patents

Integrated power grid channel rainstorm monitoring method and system based on uneven weight Download PDF

Info

Publication number
CN112526637A
CN112526637A CN202011287628.6A CN202011287628A CN112526637A CN 112526637 A CN112526637 A CN 112526637A CN 202011287628 A CN202011287628 A CN 202011287628A CN 112526637 A CN112526637 A CN 112526637A
Authority
CN
China
Prior art keywords
precipitation
data
grid
meteorological
radar
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.)
Granted
Application number
CN202011287628.6A
Other languages
Chinese (zh)
Other versions
CN112526637B (en
Inventor
李丽
章国勇
冯涛
郭俊
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd
Original Assignee
State Grid Corp of China SGCC
State Grid Hunan Electric Power Co Ltd
Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by State Grid Corp of China SGCC, State Grid Hunan Electric Power Co Ltd, Disaster Prevention and Mitigation Center of State Grid Hunan Electric Power Co Ltd filed Critical State Grid Corp of China SGCC
Priority to CN202011287628.6A priority Critical patent/CN112526637B/en
Publication of CN112526637A publication Critical patent/CN112526637A/en
Application granted granted Critical
Publication of CN112526637B publication Critical patent/CN112526637B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G01MEASURING; TESTING
    • G01WMETEOROLOGY
    • G01W1/00Meteorology
    • G01W1/10Devices for predicting weather conditions
    • GPHYSICS
    • G01MEASURING; TESTING
    • G01SRADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88Radar or analogous systems specially adapted for specific applications
    • G01S13/95Radar or analogous systems specially adapted for specific applications for meteorological use
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02ATECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Landscapes

  • Engineering & Computer Science (AREA)
  • Remote Sensing (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Physics & Mathematics (AREA)
  • Environmental & Geological Engineering (AREA)
  • Atmospheric Sciences (AREA)
  • Environmental Sciences (AREA)
  • Ecology (AREA)
  • Electromagnetism (AREA)
  • Biodiversity & Conservation Biology (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • General Physics & Mathematics (AREA)
  • Radio Relay Systems (AREA)

Abstract

The invention relates to the technical field of electric power engineering, and discloses an integrated power grid channel rainstorm monitoring method and system based on uneven weight so as to improve the monitoring precision and breadth. The method comprises the following steps: step S1, power grid channel area is defined, and ground meteorological station information in the power grid channel, meteorological radar information covering the power grid channel and synchronous satellite precipitation data are collected; step S2, constructing gridding data of three types of precipitation of a synchronous satellite, a weather radar and a ground meteorological station, and fusing precipitation information of the synchronous satellite, the weather radar and the ground meteorological station based on uneven weight; step S3, rapidly matching the fused real-time grid rainfall data with the power grid channel information to obtain real-time rainfall information in the power grid channel; and S4, carrying out real-time monitoring and early warning on the rainstorm disaster according to precipitation and real-time precipitation which have occurred at different positions in the power grid channel.

Description

Integrated power grid channel rainstorm monitoring method and system based on uneven weight
Technical Field
The invention relates to the technical field of electric power engineering, in particular to an integrated power grid channel rainstorm monitoring method and system based on uneven weight.
Background
A large amount of rainfall can be brought in a short time in the rainstorm process, so that important electric facilities such as tower foundations and transformer substations are soaked in water for a long time, and the tripping of power grid lines and the damage of important electric equipment are caused; the method is easy to cause secondary disasters such as flood, landslide and the like, so that the transmission tower is inclined and inverted, and a large-area power failure accident can be caused in serious cases. The power grid channel rainstorm real-time monitoring is carried out, the power grid rainstorm disaster occurrence and development conditions can be mastered in time, and the method has important significance in timely taking and adjusting emergency work.
At present, the main rainfall monitoring means of the meteorological department comprise a ground meteorological observation station, meteorological radar observation and satellite remote sensing monitoring. However, the power facilities are wide in points and narrow in power grid channels, the ground rain gauges and the foundation radars are scattered, the observation range is limited, and the rainfall information of the towers in the power grid channels of the cross-region cannot be covered; the satellite is used for carrying out rainstorm remote sensing inversion monitoring, although the observation range is wide, the monitoring precision is low. The single monitoring means can not meet the requirements of electric power enterprises on rainstorm monitoring in a power grid channel, and large-area power failure caused by untimely disaster disposal due to lack of disaster live information or delay easily causes great loss of national economy.
Disclosure of Invention
The invention aims to disclose an integrated power grid channel rainstorm monitoring method and system based on uneven weight so as to improve monitoring precision.
In order to achieve the purpose, the invention discloses an integrated power grid channel rainstorm monitoring method based on uneven weight, which comprises the following steps:
step S1, power grid channel area is defined, and ground meteorological station information in the power grid channel, meteorological radar information covering the power grid channel and synchronous satellite precipitation data are collected;
step S2, constructing gridding data of three types of precipitation of a synchronous satellite, a weather radar and a ground meteorological station, and fusing precipitation information of the synchronous satellite, the weather radar and the ground meteorological station based on uneven weight;
step S3, rapidly matching the fused real-time grid rainfall data with the power grid channel information to obtain real-time rainfall information in the power grid channel;
and S4, carrying out real-time monitoring and early warning on the rainstorm disaster according to precipitation and real-time precipitation which have occurred at different positions in the power grid channel.
Preferably, the gridding data processing of the step S2 specifically includes:
1) constructing a 1 km-1 km grid aiming at ground meteorological station rainfall, wherein if a meteorological station is arranged in the grid, the rainfall of the grid is the rainfall of the station, and if no meteorological station is arranged in the grid, the grid marks that the meteorological station is lack of the rainfall;
2) constructing a 1 km-1 km grid aiming at meteorological radar precipitation, wherein if precipitation data inverted by the meteorological radar exists in the grid, the precipitation of the grid is the precipitation inverted by the meteorological radar at the position, and if the precipitation data inverted by the meteorological radar does not exist in the grid, the grid marks weather radar precipitation loss;
3) the rainfall data of the wind cloud 4 synchronous satellite is 4km x 4km grids, the 4km x 4km grids are divided into 16 grids of 1km x 1km, and the rainfall data in the original 4km x 4km grids are assigned to new 16 grids of 1km x 1 km. Correspondingly, the fusion processing of step S2 includes:
according to the obtained 3 synchronous satellites with 1km x 1km grids, the meteorological radar and the ground meteorological station dewatering data, data fusion is carried out, and the basic rule is as follows:
A. if the geostationary satellite, the weather radar and the ground weather station in a certain grid all have data during the water fall, the weight coefficients of the three data are 0.05, 0.15 and 0.80 respectively;
B. if synchronous satellite and meteorological radar data exist in a certain grid, but no ground meteorological station precipitation data exist, the following two conditions are divided:
b1, if 8 grids adjacent to the grid have ground meteorological station precipitation data, assigning the ground meteorological station precipitation data adjacent to the grid, wherein the weight coefficients of the three data, namely the synchronous satellite, the meteorological radar and the ground meteorological station precipitation in the grid are 0.10, 0.40 and 0.50 respectively;
b2, if no ground meteorological station precipitation data exists in 8 grids adjacent to the grid, the weight coefficients of the geostationary satellite, the meteorological radar and the ground meteorological station precipitation data in the grid are 0.30, 0.70 and 0.00 respectively;
C. if the synchronous satellite and the ground meteorological station precipitation data exist in a certain grid but the meteorological radar data do not exist, the weight coefficients of the synchronous satellite, the meteorological radar and the ground meteorological station precipitation data in the grid are 0.10, 0.00 and 0.90 respectively;
D. if only the synchronous satellite precipitation data exists in a certain grid, no meteorological radar and ground meteorological station precipitation data exist, and the weight coefficients of the synchronous satellite precipitation data, the meteorological radar precipitation data and the ground meteorological station precipitation data in the grid are 1.00, 0.00 and 0.00 respectively.
Preferably, the fusion processing data is updated according to the least common multiple of the updating of the precipitation data of the geostationary satellite, the meteorological radar and the ground meteorological station.
In order to achieve the purpose, the invention also discloses an integrated power grid channel rainstorm monitoring system based on uneven weight, which comprises the following steps: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the steps of the corresponding method described above when executing the computer program.
The invention has the following beneficial effects:
three types of precipitation data of geostationary satellite, meteorological radar, ground meteorological station precipitation fuse, get strong and make up for the weak point based on inhomogeneous weight to effectively extended the coverage of credible data, the practicality is strong, and the rate of accuracy is high, can effectively promote accurate and the promptness of electric wire netting passageway rainstorm real-time supervision.
The present invention will be described in further detail below with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this application, illustrate embodiments of the invention and, together with the description, serve to explain the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of an integrated power grid channel rainstorm monitoring method based on uneven weight according to a preferred embodiment of the invention.
Detailed Description
The embodiments of the invention will be described in detail below with reference to the drawings, but the invention can be implemented in many different ways as defined and covered by the claims.
Example 1
The embodiment discloses an integrated power grid channel rainstorm monitoring method based on uneven weight, which comprises the following steps:
(1) electric network channel region demarcation
Important power grid channels comprise but are not limited to extra-high voltage lines for trans-provincial power transmission, 500kV or 220kV connecting lines between provinces, 220kV on important power transmission sections in the provincial power grid, upper lines and the like. The power grid channel area is defined as a long strip-shaped channel which takes a power transmission line in the power grid channel as the center and extends 1km towards the two sides of the power transmission line respectively.
(2) Basic data collection and arrangement
Collecting the concerned power grid channel information, including the start and stop positions of the power grid channel, the voltage, design information and positions of the transmission tower and the important power transformation equipment in the channel.
Collecting weather station information in the power grid channel, wherein the weather station information comprises station coordinates and rainfall updating frequency; and collecting meteorological radar information which can cover the power grid channel, and collecting synchronous satellite precipitation data.
(3) Geostationary satellite, meteorological radar and ground meteorological station rainfall information fusion based on uneven weight
The weather station rainfall information is most accurate and has the highest credibility, but only represents local data, and the data updating frequency is generally 10 minutes.
The meteorological radar can scan a large range, the space precision can reach 1km, but the meteorological radar is influenced by the terrain, the radar data has large errors in some scanning blind areas, and the data updating frequency is generally 6 minutes.
At present, the rainfall data space range of the wind and cloud 4 geostationary satellite is large, but the resolution is coarse, the resolution is 4km, the credibility of the data is the lowest of the three types of data, and the data updating frequency is 10 minutes.
The method comprises the steps of fusing rainfall information of a synchronous satellite, a meteorological radar and a ground meteorological station based on uneven weight, and firstly constructing gridding data of three types of rainfall of the synchronous satellite, the meteorological radar and the ground meteorological station.
1) The ground meteorological station precipitation constructs a 1 km-1 km grid, if a meteorological station is arranged in the grid, the precipitation of the grid is the precipitation of the station, and if no meteorological station is arranged in the grid, the grid marks the precipitation loss of the meteorological station.
2) And constructing a 1 km-1 km grid by the meteorological radar precipitation, wherein if precipitation data inverted by the meteorological radar exists in the grid, the precipitation of the grid is the precipitation inverted by the meteorological radar at the position, and if the precipitation data inverted by the meteorological radar does not exist in the grid, the grid marks the weather radar precipitation loss.
3) The rainfall data of the wind and cloud 4 synchronous satellite is 4km x 4km grids, the original 4km x 4km grids are divided into 16 1km x 1km grids, and the rainfall data in the original 4km x 4km grids are assigned to new 16 1km x 1km small grids.
4) And performing data fusion according to the obtained 3 synchronous satellites with 1km x 1km grids, the meteorological radar and the ground meteorological station precipitation data, wherein the basic rule is as follows:
if the geostationary satellite, the weather radar and the ground weather station in a certain grid all have data, the weight coefficients of the three data are 0.05, 0.15 and 0.80 respectively.
If a grid has synchronous satellite and weather radar data but no ground weather station precipitation data, the following two conditions are divided:
and i, if 8 grids adjacent to the grid have ground meteorological station precipitation data, assigning the ground meteorological station precipitation data adjacent to the grid, wherein the weight coefficients of the geostationary satellite data, the meteorological radar data and the ground meteorological station precipitation data in the grid are 0.10, 0.40 and 0.50 respectively.
ii, if no ground meteorological station precipitation data exists in 8 grids adjacent to the grid, the weight coefficients of the geostationary satellite data, the meteorological radar data and the ground meteorological station precipitation data in the grid are respectively 0.30, 0.70 and 0.00.
And thirdly, if the synchronous satellite and the ground meteorological station precipitation data exist in a certain grid but the meteorological radar data do not exist, the weight coefficients of the synchronous satellite, the meteorological radar and the ground meteorological station precipitation data in the grid are 0.10, 0.00 and 0.90 respectively.
If only the synchronous satellite precipitation data exist in a certain grid, no meteorological radar and ground meteorological station precipitation data exist, and the weight coefficients of the synchronous satellite precipitation data, the meteorological radar precipitation data and the ground meteorological station precipitation data in the grid are 1.00, 0.00 and 0.00 respectively.
And performing data fusion once every 10 minutes according to the fusion rule of the data of the precipitation of the geostationary satellite, the meteorological radar and the ground meteorological station to obtain the real-time precipitation data of the 1 km-1 km grid.
(4) Power grid channel rainstorm disaster real-time monitoring and early warning
And rapidly matching the fused 1 km-1 km grid real-time rainfall data with the long strip-shaped power grid channel information to obtain the real-time rainfall information in the power grid channel. The method comprises the steps of carrying out real-time monitoring and early warning on rainstorm disasters according to rainfall and real-time rainfall which have occurred at different positions in a power grid channel, wherein the rainstorm rainfall threshold value of the specific early warning can be adjusted by an operation and maintenance unit according to the reality.
(5) Power grid channel rainstorm disaster real-time monitoring and early warning information output and release
And (4) outputting the early warning result of the step (4), and timely issuing the early warning result to departments of safety quality, operation and maintenance, emergency repair and the like, and timely carrying out power grid channel rainstorm disaster disposal.
In summary, as shown in fig. 1, the method flow of the embodiment can be summarized as the following steps:
and step S1, the power grid channel area is defined, and ground meteorological station information in the power grid channel, meteorological radar information covering the power grid channel and synchronous satellite precipitation data are collected.
And S2, constructing gridding data of three types of precipitation of the geostationary satellite, the meteorological radar and the ground meteorological station, and fusing precipitation information of the geostationary satellite, the meteorological radar and the ground meteorological station based on the uneven weight.
And step S3, rapidly matching the fused real-time grid rainfall data with the power grid channel information to obtain real-time rainfall information in the power grid channel.
And S4, carrying out real-time monitoring and early warning on the rainstorm disaster according to precipitation and real-time precipitation which have occurred at different positions in the power grid channel.
Example 2
The embodiment discloses an integrated power grid channel rainstorm monitoring system based on uneven weight: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor implements the corresponding method steps in the above embodiments when executing the computer program.
In summary, the integrated power grid channel rainstorm monitoring method and system based on uneven weight disclosed in the embodiments of the present invention have the following beneficial effects:
three types of precipitation data of geostationary satellite, meteorological radar, ground meteorological station precipitation fuse, get strong and make up for the weak point based on inhomogeneous weight to effectively extended the coverage of credible data, the practicality is strong, and the rate of accuracy is high, can effectively promote accurate and the promptness of electric wire netting passageway rainstorm real-time supervision.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention, and those skilled in the art can make various modifications and variations (for example, values differentiated by the size of the grid). 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. An integrated power grid channel rainstorm monitoring method based on uneven weight is characterized by comprising the following steps:
step S1, power grid channel area is defined, and ground meteorological station information in the power grid channel, meteorological radar information covering the power grid channel and synchronous satellite precipitation data are collected;
step S2, constructing gridding data of three types of precipitation of a synchronous satellite, a weather radar and a ground meteorological station, and fusing precipitation information of the synchronous satellite, the weather radar and the ground meteorological station based on uneven weight;
step S3, rapidly matching the fused real-time grid rainfall data with the power grid channel information to obtain real-time rainfall information in the power grid channel;
and S4, carrying out real-time monitoring and early warning on the rainstorm disaster according to precipitation and real-time precipitation which have occurred at different positions in the power grid channel.
2. The method according to claim 1, wherein the gridding data processing of step S2 specifically includes:
1) constructing a 1 km-1 km grid aiming at ground meteorological station rainfall, wherein if a meteorological station is arranged in the grid, the rainfall of the grid is the rainfall of the station, and if no meteorological station is arranged in the grid, the grid marks that the meteorological station is lack of the rainfall;
2) constructing a 1 km-1 km grid aiming at meteorological radar precipitation, wherein if precipitation data inverted by the meteorological radar exists in the grid, the precipitation of the grid is the precipitation inverted by the meteorological radar at the position, and if the precipitation data inverted by the meteorological radar does not exist in the grid, the grid marks weather radar precipitation loss;
3) aiming at the precipitation data of the wind and cloud 4 synchronous satellite, the precipitation data is 4km x 4km grids, the 4km x 4km grids are divided into 16 grids of 1km x 1km, and the precipitation data in the original 4km x 4km grids are assigned to new 16 grids of 1km x 1 km;
the fusion process of step S2 includes:
according to the obtained 3 synchronous satellites with 1km x 1km grids, the meteorological radar and the ground meteorological station dewatering data, data fusion is carried out, and the basic rule is as follows:
A. if the geostationary satellite, the weather radar and the ground weather station in a certain grid all have data during the water fall, the weight coefficients of the three data are 0.05, 0.15 and 0.80 respectively;
B. if synchronous satellite and meteorological radar data exist in a certain grid, but no ground meteorological station precipitation data exist, the following two conditions are divided:
b1, if 8 grids adjacent to the grid have ground meteorological station precipitation data, assigning the ground meteorological station precipitation data adjacent to the grid, wherein the weight coefficients of the three data, namely the synchronous satellite, the meteorological radar and the ground meteorological station precipitation in the grid are 0.10, 0.40 and 0.50 respectively;
b2, if no ground meteorological station precipitation data exists in 8 grids adjacent to the grid, the weight coefficients of the geostationary satellite, the meteorological radar and the ground meteorological station precipitation data in the grid are 0.30, 0.70 and 0.00 respectively;
C. if the synchronous satellite and the ground meteorological station precipitation data exist in a certain grid but the meteorological radar data do not exist, the weight coefficients of the synchronous satellite, the meteorological radar and the ground meteorological station precipitation data in the grid are 0.10, 0.00 and 0.90 respectively;
D. if only the synchronous satellite precipitation data exists in a certain grid, no meteorological radar and ground meteorological station precipitation data exist, and the weight coefficients of the synchronous satellite precipitation data, the meteorological radar precipitation data and the ground meteorological station precipitation data in the grid are 1.00, 0.00 and 0.00 respectively.
3. The method of claim 2, wherein the updating of the fusion process data is performed according to the least common multiple of the geostationary satellite, meteorological radar, and ground meteorological station precipitation data updates.
4. An integrated power grid channel rainstorm monitoring system based on uneven weight: comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the steps of the method of any of the preceding claims 1 to 3 are performed by the processor when executing the computer program.
CN202011287628.6A 2020-11-17 2020-11-17 Integrated power grid channel rainstorm monitoring method and system based on uneven weight Active CN112526637B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011287628.6A CN112526637B (en) 2020-11-17 2020-11-17 Integrated power grid channel rainstorm monitoring method and system based on uneven weight

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011287628.6A CN112526637B (en) 2020-11-17 2020-11-17 Integrated power grid channel rainstorm monitoring method and system based on uneven weight

Publications (2)

Publication Number Publication Date
CN112526637A true CN112526637A (en) 2021-03-19
CN112526637B CN112526637B (en) 2022-12-06

Family

ID=74981064

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011287628.6A Active CN112526637B (en) 2020-11-17 2020-11-17 Integrated power grid channel rainstorm monitoring method and system based on uneven weight

Country Status (1)

Country Link
CN (1) CN112526637B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116434485A (en) * 2023-06-15 2023-07-14 深圳市千百炼科技有限公司 Disaster early warning method, system, equipment and medium based on multidimensional meteorological data

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104965238A (en) * 2014-11-27 2015-10-07 国网山东省电力公司应急管理中心 Power network GIS based meteorology single-station radar early warning method and system
CN105425319A (en) * 2015-09-16 2016-03-23 河海大学 Rainfall satellite rainstorm assimilation method based on ground measuring data correction
CN106324709A (en) * 2016-10-21 2017-01-11 中国人民解放军理工大学 Rainfall field reconstruction method by integrating microwave link, disdrometer, rain gauge and weather radar
CN107918165A (en) * 2016-10-09 2018-04-17 清华大学 More satellites fusion Prediction of Precipitation method and system based on space interpolation
CN108761576A (en) * 2018-05-28 2018-11-06 国网山西省电力公司电力科学研究院 A kind of X-band weather radar and precipitation station data fusion method and system
US20180372912A1 (en) * 2015-12-18 2018-12-27 Pukyong National University Industry-University Cooperation Foundation High-resolution precipitation compensation system and method
CN109518732A (en) * 2018-10-17 2019-03-26 国网湖南省电力有限公司 The cause calamity precipitation threshold value division methods and system of power grid channel Rainfall Patterns landslide disaster
CN110363327A (en) * 2019-06-04 2019-10-22 东南大学 Short based on ConvLSTM and 3D-CNN faces Prediction of Precipitation method
CN111123410A (en) * 2019-12-26 2020-05-08 国网北京市电力公司 Precipitation monitoring system and method, storage medium and processor

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104965238A (en) * 2014-11-27 2015-10-07 国网山东省电力公司应急管理中心 Power network GIS based meteorology single-station radar early warning method and system
CN105425319A (en) * 2015-09-16 2016-03-23 河海大学 Rainfall satellite rainstorm assimilation method based on ground measuring data correction
US20180372912A1 (en) * 2015-12-18 2018-12-27 Pukyong National University Industry-University Cooperation Foundation High-resolution precipitation compensation system and method
CN107918165A (en) * 2016-10-09 2018-04-17 清华大学 More satellites fusion Prediction of Precipitation method and system based on space interpolation
CN106324709A (en) * 2016-10-21 2017-01-11 中国人民解放军理工大学 Rainfall field reconstruction method by integrating microwave link, disdrometer, rain gauge and weather radar
CN108761576A (en) * 2018-05-28 2018-11-06 国网山西省电力公司电力科学研究院 A kind of X-band weather radar and precipitation station data fusion method and system
CN109518732A (en) * 2018-10-17 2019-03-26 国网湖南省电力有限公司 The cause calamity precipitation threshold value division methods and system of power grid channel Rainfall Patterns landslide disaster
CN110363327A (en) * 2019-06-04 2019-10-22 东南大学 Short based on ConvLSTM and 3D-CNN faces Prediction of Precipitation method
CN111123410A (en) * 2019-12-26 2020-05-08 国网北京市电力公司 Precipitation monitoring system and method, storage medium and processor

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
高晓荣 等: "多平台(雷达、卫星、雨量计)降水信息的融合技术初探", 《高原气象》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116434485A (en) * 2023-06-15 2023-07-14 深圳市千百炼科技有限公司 Disaster early warning method, system, equipment and medium based on multidimensional meteorological data

Also Published As

Publication number Publication date
CN112526637B (en) 2022-12-06

Similar Documents

Publication Publication Date Title
CN102118021B (en) Transmission line three-dimensional panoramic model-based fault processing method and system
CN104950187B (en) A kind of thunder and lightning analysis and early warning method and system based on power grid GIS
Reed Electric utility distribution analysis for extreme winds
CN104951993A (en) Comprehensive monitoring and early warning system based on meteorology and power grid GIS and method thereof
CN109118035B (en) Grid early warning information-based typhoon and waterlogging disaster power distribution network risk assessment method
CN104851051A (en) Dynamic-modification-combined storm rainfall fine alarming method for power grid zone
CN111612315A (en) Novel power grid disastrous gale early warning method
CN106022953A (en) Power grid infrastructure rainstorm risk assessment method
CN109543870B (en) Power transmission line tower lightning stroke early warning method based on neighborhood preserving embedding algorithm
Rawi et al. A case study on 500 kV line performance related to lightning in Malaysia
CN111738617B (en) Transformer substation risk assessment method and early warning system in heavy rainfall weather
CN105278004A (en) Meteorological condition analysis method for power grid power transmission line section
CN112526637B (en) Integrated power grid channel rainstorm monitoring method and system based on uneven weight
CN105116292A (en) Line lightning strike fault point locating method and system
Wu et al. Extension of power system early-warning defense schemes by integrating typhoon information
CN111257686A (en) Intelligent tripping analysis system based on data sharing analysis
Qiu Control measures for improving power supply reliability for mountain power supply station based on 462 mode
Huang et al. Analysis and visualization of natural threats against the security of electricity transmission system
Zelalem et al. Assessment of external insulation problems related to pollution and climatic conditions in Ethiopia
Li et al. Distribution Network Disaster Early Warning and Production Decision Support System Based on Multisource Data
CN110610272A (en) Power grid rainstorm disaster comprehensive early warning display system
Weindl et al. Climatological changes and new applications for system grid operators
Ciapessoni et al. Risk-based security assessment with big data driven probabilistic modeling for wet snow extreme events
CN115358639B (en) Offshore wind power operation risk analysis system based on data analysis
CN113435002B (en) Big data power distribution room simulation system and method

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