CN114049053A - Method and device for analyzing risk distribution of power distribution network tower under flood - Google Patents

Method and device for analyzing risk distribution of power distribution network tower under flood Download PDF

Info

Publication number
CN114049053A
CN114049053A CN202210032890.9A CN202210032890A CN114049053A CN 114049053 A CN114049053 A CN 114049053A CN 202210032890 A CN202210032890 A CN 202210032890A CN 114049053 A CN114049053 A CN 114049053A
Authority
CN
China
Prior art keywords
tower
risk
certain
follows
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.)
Pending
Application number
CN202210032890.9A
Other languages
Chinese (zh)
Inventor
蔡木良
李琼
刘娟
陈霖
朱志杰
刘蓓
陈琛
陈亚奇
吴义辉
贾玉鑫
戚沁雅
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
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
Nanchang Hangkong University
Original Assignee
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd
Nanchang Hangkong University
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, Electric Power Research Institute of State Grid Jiangxi Electric Power Co Ltd, Nanchang Hangkong University filed Critical State Grid Corp of China SGCC
Priority to CN202210032890.9A priority Critical patent/CN114049053A/en
Publication of CN114049053A publication Critical patent/CN114049053A/en
Pending legal-status Critical Current

Links

Images

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
    • 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
    • G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
    • G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
    • 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
    • Y—GENERAL 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A10/00—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE at coastal zones; at river basins
    • Y02A10/40—Controlling or monitoring, e.g. of flood or hurricane; Forecasting, e.g. risk assessment or mapping

Landscapes

  • Business, Economics & Management (AREA)
  • Human Resources & Organizations (AREA)
  • Engineering & Computer Science (AREA)
  • Economics (AREA)
  • Strategic Management (AREA)
  • Entrepreneurship & Innovation (AREA)
  • General Physics & Mathematics (AREA)
  • General Business, Economics & Management (AREA)
  • Educational Administration (AREA)
  • Marketing (AREA)
  • Theoretical Computer Science (AREA)
  • Development Economics (AREA)
  • Tourism & Hospitality (AREA)
  • Physics & Mathematics (AREA)
  • Quality & Reliability (AREA)
  • Game Theory and Decision Science (AREA)
  • Operations Research (AREA)
  • Health & Medical Sciences (AREA)
  • Public Health (AREA)
  • Water Supply & Treatment (AREA)
  • General Health & Medical Sciences (AREA)
  • Primary Health Care (AREA)
  • Alarm Systems (AREA)

Abstract

The invention discloses a method and a device for analyzing the risk distribution of a power distribution network tower under flood, wherein the method comprises the steps of establishing a tower flood disaster evaluation index system; establishing a historical data evaluation rule base according to a pole tower flood disaster evaluation index system and historical characteristic disaster information; responding to the disaster data index information of a certain tower obtained in real time, and inputting the disaster data index information of the certain tower into a historical data evaluation rule base to enable a risk factor of the certain tower to be output; updating the risk factor of a certain tower according to the risk factor of at least one tower associated with the certain tower; and judging whether the updated risk factor of a certain tower is greater than a preset threshold value or not, so as to determine the risk level of the certain tower. Risk grids are screened out by calculating risk factors according to a historical flood disaster information base, disaster risk information around the line tower can be mastered accurately, and a basis is provided for analyzing and early warning of flood disaster risks of the power distribution network line tower.

Description

Method and device for analyzing risk distribution of power distribution network tower under flood
Technical Field
The invention belongs to the technical field of risk analysis of power distribution network towers, and particularly relates to a method and a device for analyzing risk distribution of power distribution network towers in flooding.
Background
In recent years, with the high-speed innovative development of science and technology, the power system is rapidly developed as the national pillar industry, but due to the influence of natural disasters, the weak structure of the power grid is still the main factor threatening the safety of the power grid. The power transmission and distribution line pole tower is an important basic measure of a power system, is a main object which is easily impacted by a flood disaster in a natural disaster, and once the flood disaster happens, the pole tower can be aged and damaged, even serious disaster damage accidents are generated, and serious influence is caused on social economy and life. The safe and stable operation of the line tower is related to the reliable supply of electric power safety, so that the flood disaster assessment and prevention work for the tower is very necessary, the influence degree of the flood disaster on the line tower is assessed, and the establishment of a corresponding assessment index system has long-term and important practical significance.
The current research current situation discovers, flood disaster real-time supervision and early warning ability towards the shaft tower are mostly in the exploration stage at present, pole tower flood disaster early warning lacks the early warning that effectual monitoring means and degree of accuracy are high enough among the prior art, forecast key data source, only consider meteorological data information, through the incidence relation of establishing meteorological information and shaft tower flood disaster, obtain early warning information, early warning information reliability is low, flood disaster early warning's pertinence is not strong, the regional locking range of calamity is extensive, the preparation scheme of accident response is abundant inadequately, the emergent response capability of pole tower flood disaster, material deployment efficiency is low, the loss is big.
Disclosure of Invention
The invention provides a method for analyzing risk distribution of a power distribution network tower under flooding, which is used for at least solving one of the technical problems.
In a first aspect, the invention provides a method for analyzing risk distribution of power distribution network towers in flooding, which comprises the following steps: establishing a pole and tower flood disaster evaluation index system based on the flood risk level, the meteorological information, the geographic information and the underlying surface information; establishing a historical data evaluation rule base according to the pole tower flood disaster evaluation index system and historical characteristic disaster information; responding to the disaster data index information of a certain tower obtained in real time, inputting the disaster data index information of the certain tower into a historical data evaluation rule base, and outputting a risk factor of the certain tower, wherein the expression for calculating the risk factor of the certain tower is as follows:
Figure 726716DEST_PATH_IMAGE001
in the formula (I), wherein,
Figure 826259DEST_PATH_IMAGE002
is as follows
Figure 245739DEST_PATH_IMAGE003
The risk factors of the individual pole towers,
Figure 721719DEST_PATH_IMAGE004
is as follows
Figure 402099DEST_PATH_IMAGE005
The weight of each of the indices is,
Figure 547910DEST_PATH_IMAGE006
is as follows
Figure 579320DEST_PATH_IMAGE005
The score of each of the indexes is calculated,
Figure 468778DEST_PATH_IMAGE007
evaluating the index number for the flood disaster of a certain tower; updating the risk factor of the certain tower according to the risk factor of at least one tower associated with the certain tower, wherein an expression for updating the risk factor of the certain tower is as follows:
Figure 269244DEST_PATH_IMAGE008
in the formula (I), wherein,
Figure 710590DEST_PATH_IMAGE009
to be updated to the second
Figure 104662DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 656866DEST_PATH_IMAGE011
is as follows
Figure 452784DEST_PATH_IMAGE012
The tower is opposite to the first tower
Figure 65031DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 71033DEST_PATH_IMAGE013
is the first tower associated with
Figure 36715DEST_PATH_IMAGE012
The risk factors of the individual pole towers,
Figure 811773DEST_PATH_IMAGE014
is the first tower associated with
Figure 735866DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 963585DEST_PATH_IMAGE015
is as follows
Figure 123171DEST_PATH_IMAGE016
The tower is opposite to the first tower
Figure 362523DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 582152DEST_PATH_IMAGE017
is the first tower associated with
Figure 438112DEST_PATH_IMAGE016
Risk factors of individual towers; and judging whether the updated risk factor of the certain tower is greater than a preset threshold value or not, so as to determine the risk grade of the certain tower.
In a second aspect, the present invention provides a device for analyzing risk distribution of power distribution network towers in flood, including: the first establishing module is configured to establish a pole and tower flood disaster evaluation index system based on the flood risk level, the meteorological information, the geographic information and the underlying surface information; the second establishing module is configured to establish a historical data evaluation rule base according to the pole tower flood disaster evaluation index system and the historical characteristic disaster information; the output module is configured to respond to the disaster data index information of a certain tower obtained in real time and count the disasters of the certain towerInputting the index information into a historical data evaluation rule base to output the risk factor of the certain tower, wherein the expression for calculating the risk factor of the certain tower is as follows:
Figure 870230DEST_PATH_IMAGE001
in the formula (I), wherein,
Figure 619881DEST_PATH_IMAGE002
is as follows
Figure 885777DEST_PATH_IMAGE003
The risk factors of the individual pole towers,
Figure 822509DEST_PATH_IMAGE004
is as follows
Figure 464843DEST_PATH_IMAGE005
The weight of each of the indices is,
Figure 68999DEST_PATH_IMAGE006
is as follows
Figure 364852DEST_PATH_IMAGE005
The score of each of the indexes is calculated,
Figure 929825DEST_PATH_IMAGE007
evaluating the index number for the flood disaster of a certain tower; an updating module configured to update the risk factor of the certain tower according to the risk factor of at least one tower associated with the certain tower, wherein an expression for updating the risk factor of the certain tower is as follows:
Figure 969325DEST_PATH_IMAGE008
in the formula (I), wherein,
Figure 303355DEST_PATH_IMAGE009
to be updated to the second
Figure 35687DEST_PATH_IMAGE010
Wind of individual towerThe risk factor is a function of the number of the risk factors,
Figure 87957DEST_PATH_IMAGE011
is as follows
Figure 665569DEST_PATH_IMAGE012
The tower is opposite to the first tower
Figure 244318DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 22918DEST_PATH_IMAGE013
is the first tower associated with
Figure 421538DEST_PATH_IMAGE012
The risk factors of the individual pole towers,
Figure 943786DEST_PATH_IMAGE014
is the first tower associated with
Figure 134900DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 209035DEST_PATH_IMAGE015
is as follows
Figure 970318DEST_PATH_IMAGE016
The tower is opposite to the first tower
Figure 155312DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 177494DEST_PATH_IMAGE017
is the first tower associated with
Figure 297897DEST_PATH_IMAGE016
Risk factors of individual towers; and the judging module is configured to judge whether the updated risk factor of the certain tower is greater than a certain preset threshold value, so that the risk grade of the certain tower is determined.
In a third aspect, an electronic device is provided, comprising: the system comprises at least one processor and a memory which is in communication connection with the at least one processor, wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor, so that the at least one processor can execute the steps of the flood distribution network tower risk distribution analysis method according to any embodiment of the invention.
In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a computer program is stored, the computer program including program instructions, which, when executed by a computer, cause the computer to perform the steps of the method for risk distribution analysis of flooding power distribution network towers according to any of the embodiments of the present invention.
According to the method and the device for analyzing the distribution network pole tower risk distribution under the flood, the obtained flood risk distribution map and the pole tower multi-source information are combined, correlation processing is carried out on the flood disaster area grids after meshing, an evaluation index system of pole tower flood disasters is established, the risk grids are screened out according to the risk factors calculated by the historical flood disaster information base, the disaster risk information around the line pole tower can be grasped exactly, and the basis is provided for analyzing and early warning of the distribution network pole tower flood disaster risks.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
Fig. 1 is a flowchart of a risk distribution analysis method for a power distribution network tower under flooding according to an embodiment of the present invention;
fig. 2 is a flowchart of another method for analyzing risk distribution of a power distribution network tower under flooding according to an embodiment of the present invention;
fig. 3 is a block diagram of a risk distribution analysis device for a power distribution network tower under flooding according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Please refer to fig. 1, which shows a flowchart of a risk distribution analysis method for a distribution network tower under flooding according to the present application.
As shown in fig. 1, in step S101, a pole tower flood disaster evaluation index system is established based on the flood risk level, the meteorological information, the geographic information, and the underlying surface information;
in step S102, a historical data evaluation rule base is constructed according to the pole tower flood disaster evaluation index system and historical characteristic disaster information;
in step S103, in response to acquiring disaster data index information of a certain tower in real time, inputting the disaster data index information of the certain tower into a historical data evaluation rule base, so as to output a risk factor of the certain tower, where an expression for calculating the risk factor of the certain tower is as follows:
Figure 936689DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 535160DEST_PATH_IMAGE002
is as follows
Figure 677429DEST_PATH_IMAGE003
The risk factors of the individual pole towers,
Figure 93367DEST_PATH_IMAGE004
is as follows
Figure 829241DEST_PATH_IMAGE005
The weight of each of the indices is,
Figure 90458DEST_PATH_IMAGE006
is as follows
Figure 228179DEST_PATH_IMAGE005
The score of each of the indexes is calculated,
Figure 815018DEST_PATH_IMAGE007
evaluating the index number for the flood disaster of a certain tower;
in step S104, updating the risk factor of the certain tower according to the risk factor of at least one tower associated with the certain tower, where an expression for updating the risk factor of the certain tower is as follows:
Figure 897243DEST_PATH_IMAGE008
，
in the formula (I), the compound is shown in the specification,
Figure 103097DEST_PATH_IMAGE009
to be updated to the second
Figure 219957DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 712119DEST_PATH_IMAGE011
is as follows
Figure 422586DEST_PATH_IMAGE012
The tower is opposite to the first tower
Figure 556764DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 403497DEST_PATH_IMAGE013
is the first tower associated with
Figure 332139DEST_PATH_IMAGE012
The risk factors of the individual pole towers,
Figure 264323DEST_PATH_IMAGE014
is the first tower associated with
Figure 936612DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 28065DEST_PATH_IMAGE015
is as follows
Figure 2974DEST_PATH_IMAGE016
The tower is opposite to the first tower
Figure 547088DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 898435DEST_PATH_IMAGE017
is the first tower associated with
Figure 578815DEST_PATH_IMAGE016
Risk factors of individual towers;
in step S105, it is determined whether the updated risk factor of the certain tower is greater than a preset threshold, so as to determine a risk level of the certain tower.
In summary, according to the method of the embodiment, the historical data evaluation rule base is established based on the pole tower flood risk evaluation index system and the historical pole tower flood disaster feature data set, the disaster data index information of the target pole tower is input into the historical data evaluation rule base, so that the risk factor of the target pole tower corresponding to the grid is calculated, the risk factor of the target pole tower is updated according to the associated pole tower risk factor, finally, the risk values of all pole towers are obtained, and if the risk value is greater than the set threshold value, early warning is performed and early warning information is output, so that the pertinence of flood disaster early warning is enhanced, and economic loss is reduced.
Please refer to fig. 2, which shows a flowchart of another method for analyzing risk distribution of a flooding distribution network tower according to the present application.
As shown in fig. 2, the method for analyzing risk distribution of power distribution network towers under flooding specifically includes the following steps:
step 1, acquiring a flood risk distribution map.
And 2, meshing the flood disaster area based on the GIS information of the line tower.
Specifically, a gridding range is defined based on the obtained maximum value of the longitude and latitude of the flood disaster area; presetting the size of grids, and calculating the increment of the longitude and latitude of each grid according to the preset size of the grids, wherein the expression for calculating the increment of the longitude and latitude of each grid is as follows:
Figure 849259DEST_PATH_IMAGE018
，
Figure 756036DEST_PATH_IMAGE019
，
in the formula (I), the compound is shown in the specification,
Figure 770128DEST_PATH_IMAGE020
is the increase in the longitude of the grid,
Figure 445960DEST_PATH_IMAGE021
is the latitude and longitude span of the grid,
Figure 887306DEST_PATH_IMAGE022
which is the radius of the earth, is,
Figure 406012DEST_PATH_IMAGE023
is the lower limit of the latitude of the meteorological disaster area,
Figure 833582DEST_PATH_IMAGE024
is the upper limit of the latitude of the meteorological disaster area,
Figure 754133DEST_PATH_IMAGE025
an increment in the latitude of the grid;
and then according to the increment of the longitude and latitude of each grid, calculating the longitude and latitude of the central point of each grid, wherein the longitude and latitude expression of the central point of each grid is as follows:
Figure 366380DEST_PATH_IMAGE026
，
in the formula (I), the compound is shown in the specification,
Figure 716590DEST_PATH_IMAGE027
is the longitude coordinate value of the center point of the grid,
Figure 338064DEST_PATH_IMAGE028
a grid ID number corresponding to an arbitrary longitude,
Figure 847543DEST_PATH_IMAGE029
is the increase in the longitude of the grid,
Figure 771637DEST_PATH_IMAGE030
is the initial longitude value of the meteorological disaster area,
Figure 264935DEST_PATH_IMAGE031
is the latitude coordinate value of the central point of the grid,
Figure 34308DEST_PATH_IMAGE032
the grid ID number corresponding to an arbitrary latitude,
Figure 398293DEST_PATH_IMAGE025
in the case of an increasing amount of latitude of the grid,
Figure 617922DEST_PATH_IMAGE033
is the initial latitude value of the meteorological disaster area.
Calculating the expression of the grid ID number corresponding to any longitude as follows:
Figure 942724DEST_PATH_IMAGE034
，
in the formula (I), the compound is shown in the specification,
Figure 640422DEST_PATH_IMAGE035
is the longitude of any point and is the latitude of the point,
Figure 390072DEST_PATH_IMAGE029
is the increase in the longitude of the grid,
Figure 390389DEST_PATH_IMAGE036
the lower limit of the longitude of the meteorological disaster area;
calculating the expression of the grid ID number corresponding to any latitude as follows:
Figure 592700DEST_PATH_IMAGE037
，
in the formula (I), the compound is shown in the specification,
Figure 828509DEST_PATH_IMAGE038
is the latitude of any point and is,
Figure 308032DEST_PATH_IMAGE025
in the case of an increasing amount of latitude of the grid,
Figure 603884DEST_PATH_IMAGE023
the lower limit of the latitude of the meteorological disaster area.
Step 3, calculating the minimum distance between the longitude and latitude coordinates of the power distribution network tower and the grid central point to perform association;
specifically, the minimum distance of the longitude and latitude of the central point of each calculated grid is obtained, and the grid corresponding to a certain power distribution network line tower is determined based on the minimum distance.
And 4, establishing an influence evaluation index system of the flood disasters on the power distribution network tower.
It should be noted that, the distribution network shaft tower multisource information and the flood risk distribution map under the flood disaster are integrated, and characteristic parameters in four aspects are extracted, including: and carrying out association processing according to the meshing processing and the characteristic parameters.
And analyzing the processed grid information, screening out risk grids according to the risk level of the flood disasters of the power distribution network, and establishing a risk evaluation index system of the flood disasters on the power distribution network towers.
And 5, calculating the risk factor of each pole tower according to the evaluation index.
In this embodiment, the process of calculating the risk factor of each pole tower includes:
(1) acquiring historical disaster data index information of each grid, and performing fuzzy comprehensive evaluation on the historical disaster data index information, wherein the process is as follows:
let the index set as
Figure 168858DEST_PATH_IMAGE039
The corresponding weight is recorded as
Figure 473937DEST_PATH_IMAGE040
Quantizing the comment sets into F = {100,80,60,0} by adopting 4 comment sets of F = { no disaster induction risk, low disaster induction risk, medium disaster induction risk and high disaster induction risk };
the evaluation matrix An multiplied by m is determined by the membership degree of the index set to the comment set, wherein n is the number of indexes, m is the dimension of the comment set,
Figure 932600DEST_PATH_IMAGE041
indicating index
Figure 540299DEST_PATH_IMAGE042
For fuzzy sets
Figure 186044DEST_PATH_IMAGE043
The degree of membership of different elements to F is determined by a fuzzy statistical method,
Figure 170181DEST_PATH_IMAGE042
for fuzzy sets
Figure 483350DEST_PATH_IMAGE043
The membership degree determination formula is as follows:
Figure 386584DEST_PATH_IMAGE044
，
changing the fuzzy matrix into the final score on the score set through fuzzy change
Figure 660571DEST_PATH_IMAGE045
，
In the formula (I), the compound is shown in the specification,
Figure 307453DEST_PATH_IMAGE046
is as follows
Figure 350495DEST_PATH_IMAGE005
The score of each of the indexes is calculated,
Figure 159051DEST_PATH_IMAGE047
is determined by degree of membership
Figure 310547DEST_PATH_IMAGE048
The evaluation matrix is a matrix of the evaluation,
Figure 370907DEST_PATH_IMAGE049
is a set of quantized comments.
(2) Determining each index weight by entropy weight method
Figure 658669DEST_PATH_IMAGE050
The process is as follows:
data normalization
Standardizing the disaster index data subjected to intuitive fuzzification, and assuming that n indexes are given
Figure 779071DEST_PATH_IMAGE051
Wherein
Figure 152284DEST_PATH_IMAGE052
. Suppose that one finger is involvedNormalized value of the target data is
Figure 875389DEST_PATH_IMAGE053
，
Then
Figure 158603DEST_PATH_IMAGE054
，
In the formula (I), the compound is shown in the specification,
Figure 574541DEST_PATH_IMAGE055
for the corresponding after standardization
Figure 44837DEST_PATH_IMAGE005
The first in the index
Figure 837212DEST_PATH_IMAGE056
The number of the data is one,
Figure 833987DEST_PATH_IMAGE057
is the first after fuzzification
Figure 296192DEST_PATH_IMAGE005
The first in the index
Figure 643997DEST_PATH_IMAGE056
The number of the data is one,
Figure 849850DEST_PATH_IMAGE058
is the first after fuzzification
Figure 966711DEST_PATH_IMAGE005
Data set of individual indices.
Calculating the information entropy of each index
Determining an information entropy formula of a group of data according to the definition of information entropy in an information theory
Figure 599818DEST_PATH_IMAGE059
. Wherein
Figure 169339DEST_PATH_IMAGE060
If, if
Figure 303517DEST_PATH_IMAGE061
Then define
Figure 150251DEST_PATH_IMAGE062
。
In the formula (I), the compound is shown in the specification,
Figure 78892DEST_PATH_IMAGE063
is as follows
Figure 11076DEST_PATH_IMAGE005
The information entropy of each index is calculated,
Figure 948945DEST_PATH_IMAGE064
is as follows
Figure 509240DEST_PATH_IMAGE056
The data account for
Figure 749728DEST_PATH_IMAGE005
The specific gravity of each index is as follows,
Figure 293842DEST_PATH_IMAGE065
the weight of each data in the nth index is shown.
Determining the weights of the indexes
According to the calculation formula of the information entropy, the information entropy of each index is calculated as
Figure 379610DEST_PATH_IMAGE066
. Calculating the weight of each index through the information entropy:
Figure 325569DEST_PATH_IMAGE067
。
in the formula (I), the compound is shown in the specification,
Figure 596013DEST_PATH_IMAGE068
is as follows
Figure 502789DEST_PATH_IMAGE005
The weight of each of the indices is,
Figure 782461DEST_PATH_IMAGE063
is as follows
Figure 192714DEST_PATH_IMAGE005
The information entropy of each index is calculated,
Figure 634059DEST_PATH_IMAGE069
is the total number of indexes.
(3) Comprehensive evaluation for determining tower disaster risk level
And calculating historical risk factors according to the index weights:
Figure 887186DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 580336DEST_PATH_IMAGE002
is as follows
Figure 235308DEST_PATH_IMAGE003
The risk factors of the individual pole towers,
Figure 581976DEST_PATH_IMAGE004
is as follows
Figure 463344DEST_PATH_IMAGE005
The weight of each of the indices is,
Figure 819239DEST_PATH_IMAGE006
is as follows
Figure 352155DEST_PATH_IMAGE005
The score of each of the indexes is calculated,
Figure 135303DEST_PATH_IMAGE007
and evaluating the index number for the flood disaster of a certain tower.
And 6, updating the corresponding risk factors according to the calculation of the risk factors of the towers before and after the space.
In this embodiment, the tower risk factors at the front and rear positions are updated, and the expression is as follows:
Figure 972809DEST_PATH_IMAGE008
，
in the formula (I), the compound is shown in the specification,
Figure 132395DEST_PATH_IMAGE009
to be updated to the second
Figure 761960DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 856955DEST_PATH_IMAGE011
is as follows
Figure 306391DEST_PATH_IMAGE012
The tower is opposite to the first tower
Figure 4088DEST_PATH_IMAGE010
The weight of each tower (which is determined by experts based on historical experience),
Figure 363525DEST_PATH_IMAGE013
is the first tower associated with
Figure 488476DEST_PATH_IMAGE012
The risk factors of the individual pole towers,
Figure 690787DEST_PATH_IMAGE014
is the first tower associated with
Figure 801963DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 874961DEST_PATH_IMAGE015
is as follows
Figure 170813DEST_PATH_IMAGE016
The tower is opposite to the first tower
Figure 860421DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 775287DEST_PATH_IMAGE017
is the first tower associated with
Figure 968371DEST_PATH_IMAGE016
Risk factors of individual towers.
It should be noted that the reason why the corresponding risk factors are updated according to the risk factor calculation of the towers before and after the space is that after one tower is inclined, the level of the conducting wire is separately changed, the horizontal component of the span at the inclined side is reduced, and the horizontal component of the span at the reverse side is increased, so that the tension applied to the adjacent towers, particularly the reverse side tower, is increased, thereby increasing the risk of the towers; and the force can be transmitted along with the power line, so that the influence of physical mechanics (tension on adjacent towers) of front and rear towers is considered, and the tower risk factor is updated.
And 7, outputting the flood disaster risk values of all the towers.
And 8, judging whether the flood disaster risk value is greater than or equal to a threshold value.
In this embodiment, the corresponding grid disaster information with the flood disaster risk value greater than or equal to the threshold is output as disaster early warning information.
In summary, the method of the present application can achieve the following technical effects:
1. the flood disaster area is gridded through the acquired flood disaster risk distribution map, the grids corresponding to a certain power transmission and distribution line pole tower are determined based on the longitude and latitude minimum distance, and finally the disaster condition of a specific power distribution line pole tower can be analyzed and early-warned quickly and accurately.
2. Based on distribution network shaft tower multisource information under the flood disaster, draw the characteristic parameter of four aspects, include: and carrying out association processing on the flood risk level, the meteorological information, the geographic information and the underlying surface information according to the grid division and the characteristic parameters. And analyzing the processed grid information, and establishing a risk evaluation index system of the flood disaster on the power distribution network tower, so that the evaluation accuracy is increased, and the reliability of the early warning information is improved.
3. The method comprises the steps of establishing a historical data assessment rule base based on an established pole tower flood risk assessment index system and a historical pole tower flood disaster characteristic data set, calculating risk factors of corresponding poles and towers of a grid according to intuitionistically blurred grid disaster information, updating the historical data base according to the risk factors of the front pole tower and the rear pole tower, finally obtaining risk values of all the poles and towers, and if the risk values are larger than a set threshold value, performing early warning and outputting early warning information, so that the pertinence of flood disaster early warning is enhanced, and economic loss is reduced.
Please refer to fig. 3, which shows a block diagram of a risk distribution analysis apparatus for a distribution network tower under flooding conditions according to the present application.
As shown in fig. 3, the risk distribution analysis device 200 for a flooding distribution network tower includes a first establishing module 210, a second establishing module 220, an output module 230, an updating module 240, and a determining module 250.
The first establishing module 210 is configured to establish a pole and tower flood disaster evaluation index system based on the flood risk level, the meteorological information, the geographic information and the underlying surface information; the second establishing module 220 is configured to establish a historical data evaluation rule base according to the pole tower flood disaster evaluation index system and the historical characteristic disaster information; the output module 230 is configured to respond to real-time acquisition of disaster data index information of a certain tower, input the disaster data index information of the certain tower into a historical data evaluation rule base, and output a risk factor of the certain tower, where an expression for calculating the risk factor of the certain tower is:
Figure 700704DEST_PATH_IMAGE001
in the formula (I), wherein,
Figure 487394DEST_PATH_IMAGE002
is as follows
Figure 330585DEST_PATH_IMAGE003
The risk factors of the individual pole towers,
Figure 643755DEST_PATH_IMAGE004
is as follows
Figure 156776DEST_PATH_IMAGE005
The weight of each of the indices is,
Figure 820975DEST_PATH_IMAGE006
is as follows
Figure 202278DEST_PATH_IMAGE005
The score of each of the indexes is calculated,
Figure 245320DEST_PATH_IMAGE007
evaluating the index number for the flood disaster of a certain tower; an updating module 240 configured to update the risk factor of the certain tower according to the risk factor of at least one tower associated with the certain tower, where an expression for updating the risk factor of the certain tower is:
Figure 319456DEST_PATH_IMAGE008
in the formula (I), wherein,
Figure 205372DEST_PATH_IMAGE009
to be updated to the second
Figure 265732DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 287914DEST_PATH_IMAGE011
is as follows
Figure 532951DEST_PATH_IMAGE012
The tower is opposite to the first tower
Figure 515951DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 504635DEST_PATH_IMAGE013
is the first tower associated with
Figure 646903DEST_PATH_IMAGE012
The risk factors of the individual pole towers,
Figure 938208DEST_PATH_IMAGE014
is the first tower associated with
Figure 533137DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 200879DEST_PATH_IMAGE015
is as follows
Figure 197653DEST_PATH_IMAGE016
The tower is opposite to the first tower
Figure 784493DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 742084DEST_PATH_IMAGE017
is the first tower associated with
Figure 806992DEST_PATH_IMAGE016
Risk factors of individual towers; the determining module 250 is configured to determine whether the updated risk factor of the certain tower is greater than a preset threshold, so as to determine a risk level of the certain tower.
It should be understood that the modules depicted in fig. 3 correspond to various steps in the method described with reference to fig. 1. Thus, the operations and features described above for the method and the corresponding technical effects are also applicable to the modules in fig. 3, and are not described again here.
In other embodiments, an embodiment of the present invention further provides a computer-readable storage medium, where computer-executable instructions are stored in the computer-readable storage medium, and the computer-executable instructions may execute the method for analyzing risk distribution of a power distribution tower under flooding in any of the method embodiments described above;
as one embodiment, the computer-readable storage medium of the present invention stores computer-executable instructions configured to:
establishing a pole and tower flood disaster evaluation index system based on the flood risk level, the meteorological information, the geographic information and the underlying surface information;
establishing a historical data evaluation rule base according to the pole tower flood disaster evaluation index system and historical characteristic disaster information;
responding to the disaster data index information of a certain tower obtained in real time, and inputting the disaster data index information of the certain tower into a historical data evaluation rule base to enable a risk factor of the certain tower to be output;
updating the risk factor of the certain tower according to the risk factor of at least one tower associated with the certain tower;
and judging whether the updated risk factor of the certain tower is greater than a preset threshold value or not, so as to determine the risk grade of the certain tower.
The computer-readable storage medium may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area can store data and the like created according to the use of the flood distribution network pole and tower risk distribution analysis device. Further, the computer-readable storage medium may include high speed random access memory, and may also include memory, such as at least one magnetic disk storage device, flash memory device, or other non-volatile solid state storage device. In some embodiments, the computer readable storage medium optionally includes a memory remotely located from the processor, and the remote memory may be connected to the flood distribution network tower risk distribution analysis device over a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
Fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the present invention, and as shown in fig. 4, the electronic device includes: a processor 310 and a memory 320. The electronic device may further include: an input device 330 and an output device 340. The processor 310, the memory 320, the input device 330, and the output device 340 may be connected by a bus or other means, such as the bus connection in fig. 4. The memory 320 is the computer-readable storage medium described above. The processor 310 executes various functional applications and data processing of the server by running the nonvolatile software program, instructions and modules stored in the memory 320, that is, the method for analyzing risk distribution of the power distribution network tower under flooding according to the embodiment of the method is implemented. The input device 330 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the flooding distribution tower risk distribution analysis device. The output device 340 may include a display device such as a display screen.
The electronic device can execute the method provided by the embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method. For technical details that are not described in detail in this embodiment, reference may be made to the method provided by the embodiment of the present invention.
As an embodiment, the above electronic device is applied to a distribution network tower risk distribution analysis device under flooding, and is used for a client, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to:
establishing a pole and tower flood disaster evaluation index system based on the flood risk level, the meteorological information, the geographic information and the underlying surface information;
establishing a historical data evaluation rule base according to the pole tower flood disaster evaluation index system and historical characteristic disaster information;
responding to the disaster data index information of a certain tower obtained in real time, and inputting the disaster data index information of the certain tower into a historical data evaluation rule base to enable a risk factor of the certain tower to be output;
updating the risk factor of the certain tower according to the risk factor of at least one tower associated with the certain tower;
and judging whether the updated risk factor of the certain tower is greater than a preset threshold value or not, so as to determine the risk grade of the certain tower.

Claims (8)

1. A distribution network pole tower risk distribution analysis method under flooding is characterized by comprising the following steps:
establishing a pole and tower flood disaster evaluation index system based on the flood risk level, the meteorological information, the geographic information and the underlying surface information;
establishing a historical data evaluation rule base according to the pole tower flood disaster evaluation index system and historical characteristic disaster information;
responding to the disaster data index information of a certain tower obtained in real time, inputting the disaster data index information of the certain tower into a historical data evaluation rule base, and outputting a risk factor of the certain tower, wherein the expression for calculating the risk factor of the certain tower is as follows:
Figure 898158DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 690533DEST_PATH_IMAGE002
is as follows
Figure 828253DEST_PATH_IMAGE003
The risk factors of the individual pole towers,
Figure 149513DEST_PATH_IMAGE004
is as follows
Figure 497318DEST_PATH_IMAGE005
The weight of each of the indices is,
Figure 437592DEST_PATH_IMAGE006
is as follows
Figure 554453DEST_PATH_IMAGE005
The score of each of the indexes is calculated,
Figure 312193DEST_PATH_IMAGE007
evaluating the index number for the flood disaster of a certain tower;
updating the risk factor of the certain tower according to the risk factor of at least one tower associated with the certain tower, wherein an expression for updating the risk factor of the certain tower is as follows:
Figure 22660DEST_PATH_IMAGE008
，
in the formula (I), the compound is shown in the specification,
Figure 625680DEST_PATH_IMAGE009
to be updated to the second
Figure 737992DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 401055DEST_PATH_IMAGE011
is as follows
Figure 723452DEST_PATH_IMAGE012
The tower is opposite to the first tower
Figure 271108DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 96981DEST_PATH_IMAGE013
is the first tower associated with
Figure 337470DEST_PATH_IMAGE012
The risk factors of the individual pole towers,
Figure 616005DEST_PATH_IMAGE014
is the first tower associated with
Figure 91985DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 647731DEST_PATH_IMAGE015
is as follows
Figure 183755DEST_PATH_IMAGE016
The tower is opposite to the first tower
Figure 949586DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 839044DEST_PATH_IMAGE017
is the first tower associated with
Figure 639510DEST_PATH_IMAGE016
Risk factors of individual towers;
and judging whether the updated risk factor of the certain tower is greater than a preset threshold value or not, so as to determine the risk grade of the certain tower.
2. The method according to claim 1, wherein the at least one tower associated with the certain tower is:
and at least one tower which is adjacent to the certain tower and is positioned at the front position, the rear position, the left position or the right position of the certain tower.
3. The method of claim 1, wherein the calculating the first step comprises
Figure 221801DEST_PATH_IMAGE005
The expression of the weight of each index is:
Figure 474928DEST_PATH_IMAGE018
，
in the formula (I), the compound is shown in the specification,
Figure 292711DEST_PATH_IMAGE019
is as follows
Figure 557471DEST_PATH_IMAGE005
The weight of each of the indices is,
Figure 435297DEST_PATH_IMAGE020
is as follows
Figure 175720DEST_PATH_IMAGE005
The information entropy of each index is calculated,
Figure 672560DEST_PATH_IMAGE021
is the total number of indexes.
4. The method of claim 3, wherein the calculating the first step comprises
Figure 182039DEST_PATH_IMAGE005
The expression of the information entropy of each index is as follows:
Figure 840553DEST_PATH_IMAGE022
，
in the formula (I), the compound is shown in the specification,
Figure 68272DEST_PATH_IMAGE020
is as follows
Figure 962279DEST_PATH_IMAGE005
The information entropy of each index is calculated,
Figure 732789DEST_PATH_IMAGE021
is the total number of the indexes,
Figure 421259DEST_PATH_IMAGE023
is as follows
Figure 605116DEST_PATH_IMAGE024
The data account for
Figure 568393DEST_PATH_IMAGE005
Specific gravity of each index;
Figure 786884DEST_PATH_IMAGE025
，
Figure 52781DEST_PATH_IMAGE026
，
in the formula (I), the compound is shown in the specification,
Figure 989513DEST_PATH_IMAGE027
for the corresponding after standardization
Figure 631846DEST_PATH_IMAGE005
The first in the index
Figure 970424DEST_PATH_IMAGE024
The number of the data is one,
Figure 531855DEST_PATH_IMAGE028
is the first after fuzzification
Figure 831250DEST_PATH_IMAGE005
The first in the index
Figure 136329DEST_PATH_IMAGE024
The number of the data is one,
Figure 329413DEST_PATH_IMAGE029
is the first after fuzzification
Figure 937112DEST_PATH_IMAGE005
Data set of individual indices.
5. The method of claim 1, wherein the calculating the first step comprises
Figure 848436DEST_PATH_IMAGE005
The expression of the score of each index is:
Figure 566993DEST_PATH_IMAGE030
，
in the formula (I), the compound is shown in the specification,
Figure 473638DEST_PATH_IMAGE031
is as follows
Figure 986659DEST_PATH_IMAGE005
The score of each of the indexes is calculated,
Figure 650859DEST_PATH_IMAGE032
is determined by degree of membership
Figure 32161DEST_PATH_IMAGE033
The evaluation matrix is a matrix of the evaluation,
Figure 340783DEST_PATH_IMAGE034
is a set of quantized comments.
6. The utility model provides a distribution network shaft tower risk distribution analytical equipment under flood which characterized in that includes:
the first establishing module is configured to establish a pole and tower flood disaster evaluation index system based on the flood risk level, the meteorological information, the geographic information and the underlying surface information;
the second establishing module is configured to establish a historical data evaluation rule base according to the pole tower flood disaster evaluation index system and the historical characteristic disaster information;
the output module is configured to respond to the fact that disaster data index information of a certain tower is obtained in real time, input the disaster data index information of the certain tower into a historical data evaluation rule base, and enable risk factors of the certain tower to be output, wherein the expression for calculating the risk factors of the certain tower is as follows:
Figure 149339DEST_PATH_IMAGE001
，
in the formula (I), the compound is shown in the specification,
Figure 769676DEST_PATH_IMAGE002
is as follows
Figure 95615DEST_PATH_IMAGE003
The risk factors of the individual pole towers,
Figure 117798DEST_PATH_IMAGE004
is as follows
Figure 97255DEST_PATH_IMAGE005
The weight of each of the indices is,
Figure 611413DEST_PATH_IMAGE006
is as follows
Figure 334519DEST_PATH_IMAGE005
The score of each of the indexes is calculated,
Figure 352153DEST_PATH_IMAGE007
evaluating the index number for the flood disaster of a certain tower;
an updating module configured to update the risk factor of the certain tower according to the risk factor of at least one tower associated with the certain tower, wherein an expression for updating the risk factor of the certain tower is as follows:
Figure 768091DEST_PATH_IMAGE008
，
in the formula (I), the compound is shown in the specification,
Figure 97441DEST_PATH_IMAGE009
to be updated to the second
Figure 889817DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 496379DEST_PATH_IMAGE011
is as follows
Figure 83218DEST_PATH_IMAGE012
The tower is opposite to the first tower
Figure 165443DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 371297DEST_PATH_IMAGE013
is the first tower associated with
Figure 222578DEST_PATH_IMAGE012
The risk factors of the individual pole towers,
Figure 245898DEST_PATH_IMAGE014
is the first tower associated with
Figure 425206DEST_PATH_IMAGE010
The risk factors of the individual pole towers,
Figure 293805DEST_PATH_IMAGE015
is as follows
Figure 530751DEST_PATH_IMAGE016
The tower is opposite to the first tower
Figure 69180DEST_PATH_IMAGE010
The weight of each tower is calculated by the weight of each tower,
Figure 149435DEST_PATH_IMAGE017
is the first tower associated with
Figure 821725DEST_PATH_IMAGE016
Risk factors of individual towers;
and the judging module is configured to judge whether the updated risk factor of the certain tower is greater than a certain preset threshold value, so that the risk grade of the certain tower is determined.
7. An electronic device, comprising: at least one processor, and a memory communicatively coupled to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the method of any of claims 1 to 5.
8. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the method of any one of claims 1 to 5.
CN202210032890.9A 2022-01-12 2022-01-12 Method and device for analyzing risk distribution of power distribution network tower under flood Pending CN114049053A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202210032890.9A CN114049053A (en) 2022-01-12 2022-01-12 Method and device for analyzing risk distribution of power distribution network tower under flood

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202210032890.9A CN114049053A (en) 2022-01-12 2022-01-12 Method and device for analyzing risk distribution of power distribution network tower under flood

Publications (1)

Publication Number Publication Date
CN114049053A true CN114049053A (en) 2022-02-15

Family

ID=80196314

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202210032890.9A Pending CN114049053A (en) 2022-01-12 2022-01-12 Method and device for analyzing risk distribution of power distribution network tower under flood

Country Status (1)

Country Link
CN (1) CN114049053A (en)

Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103985057A (en) * 2014-05-27 2014-08-13 煤炭科学研究总院 Coal mine safety risk evaluation or loss evaluation method and device
CN106875105A (en) * 2017-01-23 2017-06-20 东北大学 A kind of power distribution network differentiation planing method for considering combined failure risk
CN108564263A (en) * 2018-04-02 2018-09-21 国网安徽省电力有限公司电力科学研究院 One kind is for the disaster-stricken prediction technique of electric power line pole tower under squall line environment
US20190051146A1 (en) * 2017-08-09 2019-02-14 Institute Of Mountain Hazards And Environment, Chinese Academy Of Sciences Three-dimensional multi-point multi-index early warning method for risk at power grid tower in landslide section
CN109741071A (en) * 2019-01-03 2019-05-10 江苏方天电力技术有限公司 A kind of large power customers tariff recovery methods of risk assessment based on Information Entropy
WO2021169038A1 (en) * 2020-02-28 2021-09-02 青岛理工大学 Deep foundation pit blasting vibration velocity risk level big data evaluation method
CN113537846A (en) * 2021-09-17 2021-10-22 国网江西省电力有限公司电力科学研究院 Meteorological disaster-based risk analysis method and system for power transmission and distribution line tower
CN113570226A (en) * 2021-07-20 2021-10-29 中交第一公路勘察设计研究院有限公司 Method for evaluating occurrence probability grade of tunnel water inrush disaster in fault fracture zone
CN113869804A (en) * 2021-12-02 2021-12-31 国网江西省电力有限公司电力科学研究院 Power grid equipment risk early warning method and system under flood disaster

Patent Citations (9)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN103985057A (en) * 2014-05-27 2014-08-13 煤炭科学研究总院 Coal mine safety risk evaluation or loss evaluation method and device
CN106875105A (en) * 2017-01-23 2017-06-20 东北大学 A kind of power distribution network differentiation planing method for considering combined failure risk
US20190051146A1 (en) * 2017-08-09 2019-02-14 Institute Of Mountain Hazards And Environment, Chinese Academy Of Sciences Three-dimensional multi-point multi-index early warning method for risk at power grid tower in landslide section
CN108564263A (en) * 2018-04-02 2018-09-21 国网安徽省电力有限公司电力科学研究院 One kind is for the disaster-stricken prediction technique of electric power line pole tower under squall line environment
CN109741071A (en) * 2019-01-03 2019-05-10 江苏方天电力技术有限公司 A kind of large power customers tariff recovery methods of risk assessment based on Information Entropy
WO2021169038A1 (en) * 2020-02-28 2021-09-02 青岛理工大学 Deep foundation pit blasting vibration velocity risk level big data evaluation method
CN113570226A (en) * 2021-07-20 2021-10-29 中交第一公路勘察设计研究院有限公司 Method for evaluating occurrence probability grade of tunnel water inrush disaster in fault fracture zone
CN113537846A (en) * 2021-09-17 2021-10-22 国网江西省电力有限公司电力科学研究院 Meteorological disaster-based risk analysis method and system for power transmission and distribution line tower
CN113869804A (en) * 2021-12-02 2021-12-31 国网江西省电力有限公司电力科学研究院 Power grid equipment risk early warning method and system under flood disaster

Non-Patent Citations (2)

* Cited by examiner, † Cited by third party
Title
谢从珍 等: "基于多维关联信息融合的架空输电线路雷害风险评估方法", 《中国电机工程学报》 *
邓红雷 等: "基于层次分析-熵权组合法的架空输电线路综合运行风险评估", 《电力系统保护与控制》 *

Similar Documents

Publication Publication Date Title
CN102629294B (en) Probability evaluation method of failure caused by typhoon to power transmission line
US20220113343A1 (en) Infrared zero value diagnosis method and system for porcelain insulator string
CN109544399B (en) Power transmission equipment state evaluation method and device based on multi-source heterogeneous data
CN107944590B (en) Method and equipment for analyzing and forecasting fishing situations
CN106469356A (en) Transmission facility state evaluation based on multidimensional data and risk analysis method and device
CN103606109B (en) A kind of safe operation of electric network risk integrative assessment method based on evaluation object
CN113689053B (en) Strong convection weather overhead line power failure prediction method based on random forest
CN111047169A (en) Fault analysis and detection system for power grid dispatching
CN114723285A (en) Power grid equipment safety evaluation prediction method
CN116050599A (en) Line icing fault prediction method, system, storage medium and equipment
CN117191147A (en) Flood discharge dam water level monitoring and early warning method and system
CN115587719A (en) Large power grid static security risk analysis method based on dense power transmission channel disasters
CN113537846B (en) Meteorological disaster-based risk analysis method and system for power transmission and distribution line tower
CN116523140A (en) Method and device for detecting electricity theft, electronic equipment and storage medium
CN115577011A (en) Power transmission line monitoring method and related equipment
CN117113157B (en) Platform district power consumption fault detection system based on artificial intelligence
CN118193954A (en) Power distribution network abnormal data detection method and system based on edge calculation
CN113657610A (en) Hail climate characteristic prediction method based on random forest
CN114049053A (en) Method and device for analyzing risk distribution of power distribution network tower under flood
CN117034149A (en) Fault processing strategy determining method and device, electronic equipment and storage medium
CN111506636A (en) System and method for analyzing residential electricity consumption behavior based on autoregressive and neighbor algorithm
CN116341739A (en) Flood loss pre-assessment method, device, equipment and medium
CN116541780A (en) Power transmission line galloping early warning method, device, equipment and storage medium
Li et al. Prediction Algorithm of Wind Waterlogging Disaster in Distribution Network Based on Multi‐Source Data Fusion
CN117131947B (en) Overhead transmission line fault prediction method, device, equipment and storage medium

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
RJ01 Rejection of invention patent application after publication

Application publication date: 20220215

RJ01 Rejection of invention patent application after publication