CN106022953A - Power grid infrastructure rainstorm risk assessment method - Google Patents
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Abstract
The invention relates to a power grid infrastructure rainstorm risk assessment method which sequentially comprises the steps of data collection, risk assessment, and risk rating. The method has the following advantages: power grid infrastructure rainstorm risk assessment is conducted through data collection, risk assessment and risk rating; disaster condition data, rainstorm data, social economic data, basic geographic information data and grid distribution data can be used for reference; four elements causing rainstorm and flood risk to the power grid, including the degree of disaster-inducing factors, the sensitivity of disaster-breeding environment, the vulnerability of hazard-affected body and the ability to prevent and fight against disasters, can be calculated efficiently and accurately; and the rainstorm risk can be assessed comprehensively, and the power grid rainstorm water-logging disaster risk index is divided into different grades through risk rating, which facilitates observation by operators.
Description
Technical field
The present invention relates to electrical network facilities heavy rain methods of risk assessment.
Background technology
Rainstorm Flood comes from, to the one side of electric network influencing, the destruction that wind-force brings, and such as the shaft tower at towards Haikou and the high mountain air port of Landed Typhoon direction of advance, because being attacked by exceeding the violent typhoon of design wind speed, causes down bar, bending, causes line tripping；In transformer station, main transformer downlead is caused windage yaw to discharge by typhoon influence; main transformer is caused to trip; another aspect come from Landed Typhoon after heavy showers through often bringing; rain drop erosion overhead line structures basis; causing shaft tower to tilt even tower, transformer station, switchgear house particularly underground switching station is brought and has a strong impact on by flood, mud-rock flow, causes secondary device such as terminal box, straight-flow system to intake; causing protective relaying device cisco unity malfunction or malfunction, tripping, the most whole transformer station stops transport.
Heavy rain infringement Substation Electric Equipment insulation, cause equipment operation exception or fault, heavy rain during high wind often rainfall is anxious greatly, direction is inclined, it some times happens that local spout wind and rain, the weather seal of Substation Electric Equipment is constituted bigger threat, the especially roof of high-voltage switch gear room, the door and window of relay protection chamber, outdoor breaker, the mechanism case of disconnecting switch, terminal box etc., these significant points generation seepage rain, it is possible to cause high pressure equipment exterior insulation flashover to discharge, or cause secondary control loop ground connection, short trouble, even result in protection and switch malfunction tripping operation, the transformer station being in the relatively low region of flood control standard may also suffer from big flood, the serious threat of mud-rock flow, there is the danger of water logging transformer station in the transformer station being in the serious location of urban waterlogging.
The method of availability risk assessment mainly has calamity source appraisal procedure based on index system, calamity source appraisal procedure based on history the condition of a disaster data, calamity source appraisal procedure based on RS and GIS techniques, calamity source appraisal procedure based on scenario analysis, methods of risk assessment based on event Evolution Dynamics.
Along with the quickening of urban development construction in recent years and frequently occurring of strong convective weather, urban waterlogging occurs in that much the newest feature.The phenomenons such as the paralysis of electrical network that it causes, river course spread, traffic jam are more and more frequent, become one of new difficult problem that current city guard against disaster work faces.Accordingly, it would be desirable to the specific features of urban waterlogging disaster is analyzed, thus carry out more careful risk assessment study work, set up scientific and normal perfect heavy rain urban waterlogging Hazard Risk Assessment system.With risk assessment for guiding, production management department at different levels implementation is instructed to take precautions against natural calamities targetedly, build, run and retrofit work.But, the urban waterlogging model caused Rainfall Disaster at present realizes essentially by rainwash, hydrodynamics, and the waterlogging that one of focus that meteorological department is main is caused by heavy rain exactly amasss damage caused by waterlogging evil, and it is carried out risk assessment work.Along with the raising day by day to risk assessment accuracy requirement, there are higher requirement, increasing Traditional solutions and technology cannot meet demand at urban waterlogging fine degree, space manifestation mode, all aspects such as ageing now.The following is in risk assessment, some shortcomings that all kinds of methods exist:
1. risk assessment based on index system
Methods of risk assessment based on index system be current most widely used general be also most to have the method with objection.Generally evaluation index choose be limited to data can availability, make when index for selection it may occur that the phenomenon of " with point as face ", process may lack certain representativeness and comprehensive.And due to the contact between each risk assessment element of complicated disaster, and its evolutionary process can not very well be simulated, therefore the method is difficult to embody the uncertainty of disaster and dynamic, and the result of risk assessment exists not parasexuality.It addition, assessment result can only have overall embodiment to risk class, and only possess index effect, it is impossible to the spatial distribution characteristic to calamity source completely carries out labor, more be applicable to large scale risk assessment, be not appropriate for carrying out in small-scale region.Finally for the motility of estimation flow, in terms of standardization and assessment result credibility, the method equally exists certain limitation.
2. assessments based on history the condition of a disaster data
Substantially experienced by extreme value Evaluation Method, probability assessment method and 3 stages of THE FUZZY EVALUATING METHOD.Wherein, extreme value Evaluation Method is that at utmost (calamity degree or the calamity are damaged) record to the urban waterlogging disaster caused by heavy rain in history is estimated.Although the method is easy to use, but generally there is obvious deviation when risk assessment；Probability assessment method is the probability distribution rule by adding up disaster samples, utilize outcross probability to obtain calamity degree or calamity is damaged, and measuring in this, as risk, the stochastic uncertainty of Disaster Event can be estimated by the method, assessment result is reliable, but the method is when running into data sample and be less and cannot accurately obtain the probability distribution of sample, assessment result is it would appear that bigger deviation；THE FUZZY EVALUATING METHOD is with historical summary data as evidence, to the fuzzy uncertain in calamity source because have fuzzy uncertainty, and fuzzy set can be utilized on the premise of not knowing parameter cutting cloth really to express risk, but mostly the method assessment result is relation or fuzzy set, it is impossible to directly compare.
3. calamity source based on RS and GIS techniques assessment
Often it is limited to the spatial resolution degree of remote sensing images, is relatively more suitable for the research of Large-scale areas, likewise, it may be difficult to compare casualty loss estimation accurately and risk assessment in the region of little yardstick.
4. risk assessment based on scenario analysis
Methods of risk assessment based on scenario analysis equally exists certain limitation.First, owing to the method is when combining hazard-affected body and analyzing, the geographical background data (geology, landforms, landform, the network of waterways etc.) in region is required higher by it, calculate complexity, workload is big, Disaster Assessment can only be carried out at middle or small yardstick, be not suitable for carrying out in large scale (scope) region；It addition, the border scenarios of the model employed in scenario analysis sets and generally lacks scientific basis, the research of the extreme risk sight of probability level certain to region is the most not enough；Finally, scenario analysis is mostly the simulation to calamity source scene, and to occurring that the concrete simulation effectively performed that certain calamity source is assessed also is not directed to.
Summary of the invention
The technical problem to be solved in the present invention is to provide electrical network facilities heavy rain methods of risk assessment, solves availability risk appraisal procedure and there is limitation and the problem of analytical effect difference.
In order to solve above-mentioned technical problem, the present invention is achieved by the following technical solutions: electrical network facilities heavy rain methods of risk assessment, comprises the following steps successively:
A) collection of data: the condition of a disaster data, rain data, social economy's data, Fundamental Geographic Information Data and electrical network distributed data are collected, the condition of a disaster data is heavy rain electrical network disaster data, take the maximum disaster data of impact and add up the electric power the condition of a disaster frequency with block as unit, rain data is the Daily rainfall amount statistics of weather station, electric power weather monitoring station, social economy's data is power consumption in the land area of street unit and district, Fundamental Geographic Information Data includes that water system and dem data, electrical network distributed data are block transformer station distributed points statistics；
B) risk assessment: the data collected according to step a) calculates Flood inducing factors degree by, pregnant calamity environmental sensitivity yz, hazard-affected body vulnerability czt, ability fznl of preventing and fighting natural adversities respectively；
C) risk rating: the data assessing step b) calculate waterlogging calamity source index b ynl, and by nature breakpoint staging, waterlogging electrical network calamity source index are divided into high risk area, secondary high risk area, medium risk district, secondary low-risk district and low-risk district；
And bynl=(bywe)(yzwh)(cztws)(10-fznl)wr
Wherein: we is the weight of Flood inducing factors degree, wh is the weight of pregnant calamity environmental sensitivity, and ws is the weight of hazard-affected body vulnerability, and wr is the weight of ability of preventing and fighting natural adversities.
Preferably, the pregnant calamity environmental sensitivity in step b) is depression, and the detection in depression is by quickly flooding
Method realizes, and method of quickly flooding comprises the following steps successively:
A. DEM border grid is extracted；
B. the DEM border grid that step A is extracted is inserted queue；
C. whether the queue in detecting step B is empty, if detection is not empty, then enters step D, if being detected as sky, then enters step F；
D. the minimum grid in step B queue is taken, subsequently into step E；
E. search for the upstream region of minimum grid, update inner boundary and return step C；
F. whether detection inner boundary is empty, if detection is not empty, then enters step G, if being detected as sky, then enters step J；
G. the minimum grid of inner boundary in step F is taken；
H. fill depression and record scope and the outlet in depression；
I. the upstream region in search step H record depression, updates inner boundary and returns F step；
J. depression detection terminates.
Preferably, the search upstream region in step E and step I comprises the following steps the most successively:
A. eight neighborhood grid of current mesh are searched for；
B. one of them in eight neighborhood grid in selecting step a；
C. judge that the neighborhood grid chosen in step b is the most processed, if it is determined that process, then enter step d, if it is determined that untreated, then enter step e；
D. judge that step b neighborhood is the most processed, if it is determined that processed, then enter step g, if it is determined that untreated, then return step b；
E. judge that the neighborhood grid chosen in step b, whether less than current mesh, if the judgment is Yes, then enters step d, if the judgment is No, then enters step f；
F. neighborhood grid added priority query and be labeled as processed, and entering step d；
G. judge whether queue is empty, if it is determined that be not empty, then enter step h, if it is determined that empty, then enter step i；
H. take elevation minimum grid in queue, set current mesh, and return step a；
I. go up outbound search to complete.
Preferably, step H is filled depression to comprise the following steps successively:
A). depression outlet is arranged to current mesh；
B). search step A) in eight neighborhood grid of current mesh；
C). selecting step B) in one of them in eight neighborhood grid；
D). judge step C) in the neighborhood grid chosen the most processed, if it is determined that process, then enter step E), if it is determined that untreated, then enter step F)；
E). judge step C) in neighborhood lattice the most processed, if it is determined that processed, then enter step H), if it is determined that untreated, then return step C)；
F). judge neighborhood≤current mesh situation, if the judgment is Yes, then enter step G), if the judgment is No, then enter step E)；
G). neighborhood grid added fifo queue and is labeled as processed, and entering step E)；
H). judge whether queue is empty, if it is determined that be not empty, then enter step I), if it is determined that empty, then enter step J)；
I). choose first grid of queue and be set as current mesh, and returning step B)；
J). depression has been filled.
Preferably, hazard-affected body vulnerability in step b) passes through czt=Su/Vr, Ht >=Hr, wherein: Su is current accumulated value, Vr is critical reservoir storage, Ht is theoretical maximum depth of accumulated water, Hr is the maximum depth of accumulated water that can bear, adapt to different power system carriers, as sent out, become, defeated, join the electric power facility of link, power plant such as the link that generates electricity, the electric substation of power transformation link, the electric tower of transmission of electricity link, the distribution substation of distribution link, mean that when Vr reaches critical reservoir storage and given carrying object is produced risk, depression landform is applied to the risk factor of self, critical reservoir storage is the least, occur the time reaching critical depth of water during hydrops the shortest, thus waterlogging risk is the biggest.
In sum, advantages of the present invention: pass through collection of data, risk assessment, the method of risk rating realizes the risk assessment of electrical network facilities heavy rain, can be by the condition of a disaster data, rain data, social economy's data, Fundamental Geographic Information Data and electrical network distributed data carry out reference, can be efficiently, calculate the Flood inducing factors degree causing Rainstorm Flood electrical network disaster exactly, pregnant calamity environmental sensitivity, hazard-affected body vulnerability, ability of preventing and fighting natural adversities four elements, comprehensively heavy rain risk can be estimated, and pass through risk rating, realize waterlogging electrical network calamity source index and divide different grades, it is easy to the observation of effect personnel.
Detailed description of the invention
Electrical network facilities heavy rain methods of risk assessment, comprises the following steps successively:
A) collection of data: the condition of a disaster data, rain data, social economy's data, Fundamental Geographic Information Data and electrical network distributed data are collected, the condition of a disaster data is heavy rain electrical network disaster data, take the maximum disaster data of impact and add up the electric power the condition of a disaster frequency with block as unit, rain data is the Daily rainfall amount statistics of weather station, electric power weather monitoring station, social economy's data is power consumption in the land area of street unit and district, Fundamental Geographic Information Data includes that water system and dem data, electrical network distributed data are block transformer station distributed points statistics；
B) risk assessment: the data collected according to step a) calculates Flood inducing factors degree by, pregnant calamity environmental sensitivity yz, hazard-affected body vulnerability czt, ability fznl of preventing and fighting natural adversities respectively；
C) risk rating: the data assessing step b) calculate waterlogging calamity source index b ynl, and by nature breakpoint staging, waterlogging electrical network calamity source index are divided into high risk area, secondary high risk area, medium risk district, secondary low-risk district and low-risk district；
And bynl=(bywe)(yzwh)(cztws)(10-fznl)wr
Wherein: we is the weight of Flood inducing factors degree, wh is the weight of pregnant calamity environmental sensitivity, and ws is the weight of hazard-affected body vulnerability, and wr is the weight of ability of preventing and fighting natural adversities.
Pregnant calamity environmental sensitivity in step b) is depression, and the detection in depression realizes by quickly flooding method,
Method of quickly flooding comprises the following steps successively:
A. DEM border grid is extracted；
B. the DEM border grid that step A is extracted is inserted queue；
C. whether the queue in detecting step B is empty, if detection is not empty, then enters step D, if being detected as sky, then enters step F；
D. the minimum grid in step B queue is taken, subsequently into step E；
E. search for the upstream region of minimum grid, update inner boundary and return step C；
F. whether detection inner boundary is empty, if detection is not empty, then enters step G, if being detected as sky, then enters step J；
G. the minimum grid of inner boundary in step F is taken；
H. fill depression and record scope and the outlet in depression；
I. the upstream region in search step H record depression, updates inner boundary and returns F step；
J. depression detection terminates.
Search upstream region in step E and step I comprises the following steps the most successively:
A. eight neighborhood grid of current mesh are searched for；
B. one of them in eight neighborhood grid in selecting step a；
C. judge that the neighborhood grid chosen in step b is the most processed, if it is determined that process, then enter step d, if it is determined that untreated, then enter step e；
D. judge that step b territory lattice are the most processed, if it is determined that processed, then enter step g, if it is determined that untreated, then return step b；
E. judge that the neighborhood grid chosen in step b, whether less than current mesh, if the judgment is Yes, then enters step d, if the judgment is No, then enters step f；
F. neighborhood grid added priority query and be labeled as processed, and entering step d；
G. judge whether queue is empty, if it is determined that be not empty, then enter step h, if it is determined that empty, then enter step i；
H. take elevation minimum grid in queue, set current mesh, and return step a；
I. go up outbound search to complete.
Elevation: from altitude data, divides the grid of 2m*2m, uses 8 lattice point elevation standard deviations around to represent that hypsography changes, and as influence of topography index, elevation is the lowest, standard deviation is the least, represents and more advantageously forms damage caused by waterlogging, and influence value is the biggest；
Step H is filled depression comprise the following steps successively:
A). depression outlet is arranged to current mesh；
B). search step A) in eight neighborhood grid of current mesh；
C). selecting step B) in one of them in eight neighborhood grid；
D). judge step C) in the neighborhood grid chosen the most processed, if it is determined that process, then enter step E), if it is determined that untreated, then enter step F)；
E). judge step C) in neighborhood lattice the most processed, if it is determined that processed, then enter step H), if it is determined that untreated, then return step C)；
F). judge neighborhood≤current mesh situation, if the judgment is Yes, then enter step G), if the judgment is No, then enter step E)；
G). neighborhood grid added fifo queue and is labeled as processed, and entering step E)；
H). judge whether queue is empty, if it is determined that be not empty, then enter step I), if it is determined that empty, then enter step J)；
I). choose first grid of queue and be set as current mesh, and returning step B)；
J). depression has been filled.
Hazard-affected body vulnerability in step b) passes through czt=Su/Vr, Ht >=Hr, wherein: Su is current accumulated value, Vr is critical reservoir storage, Ht is theoretical maximum depth of accumulated water, Hr is the maximum depth of accumulated water that can bear, adapt to different power system carriers, as sent out, become, defeated, join the electric power facility of link, power plant such as the link that generates electricity, the electric substation of power transformation link, the electric tower of transmission of electricity link, the distribution substation of distribution link, mean that when Vr reaches critical reservoir storage and given carrying object is produced risk, depression landform is applied to the risk factor of self, critical reservoir storage is the least, occur the time reaching critical depth of water during hydrops the shortest, thus waterlogging risk is the biggest.
Use Su=Vr as the index weighing waterlogging risk, when carrying out depression detection by the quickly method of flooding, can obtain scope and the exit point in depression, the elevation minimum point of scanning region, depression grid, theoretical maximum depth of accumulated water Ht is the difference of exit point elevation and minimum point elevation；
Depression minimum point elevation just obtains the critical elevation of waterlogging risk plus critical depth of water (the maximum depth of accumulated water that carrying object can bear), the grid in scanning region, depression, statistics is less than grid and the depth displacement of critical elevation, being calculated to obtain the reservoir storage of a mesh region by grid area and depth displacement, cumulative all grid just can obtain critical reservoir storage Vr；
From the border grid in depression, searching for the upstream grid of each border grid by the opposite direction of water (flow) direction, add up the upstream grid number of all borders grid after completing search, the area being multiplied by grid just obtains upstream catchment area Su in depression.
The ability of preventing and fighting natural adversities is to be resisted Rainfall Disaster and recovery extent by disaster area, and all kinds of hazard-affected carriers also differ due to himself structure, function, the difference of spatial distribution, its anti-disaster ability.
In addition to above preferred embodiment, the present invention also has other embodiment, and those skilled in the art can be variously modified according to the present invention and deform, and without departing from the spirit of the present invention, all should belong to scope defined in claims of the present invention.
Claims (5)
1. electrical network facilities heavy rain methods of risk assessment, it is characterised in that: comprise the following steps successively:
A) collection of data: to the condition of a disaster data, rain data, social economy's data, Fundamental Geographic Information Data and
Electrical network distributed data is collected, and the condition of a disaster data is heavy rain electrical network disaster data, takes the calamity that impact is maximum
Evil data also add up the electric power the condition of a disaster frequency with block as unit, and rain data is weather station, electric power gas
As the Daily rainfall amount of monitoring station is added up, in social economy's data is the land area of street unit and district
Power consumption, Fundamental Geographic Information Data includes that water system and dem data, electrical network distributed data are that block becomes
Power station distributed points statistics；
B) risk assessment: the data collected according to step a) calculates Flood inducing factors degree by, pregnant calamity ring respectively
Border sensitivity yz, hazard-affected body vulnerability czt, ability fznl of preventing and fighting natural adversities；
C) risk rating: the data assessing step b) calculate waterlogging calamity source index b ynl, and
By natural breakpoint staging, waterlogging electrical network calamity source index is divided into high risk area, secondary
High risk area, medium risk district, secondary low-risk district and low-risk district；
And bynl=(bywe)(yzwh)(cztws)(10-fznl)wr
Wherein: we is the weight of Flood inducing factors degree, wh is the weight of pregnant calamity environmental sensitivity, and ws is for holding
The weight of calamity body vulnerability, wr is the weight of ability of preventing and fighting natural adversities.
Electrical network facilities heavy rain methods of risk assessment the most according to claim 1, it is characterised in that: step b)
In pregnant calamity environmental sensitivity be depression, and the detection in depression by quickly flood method realize, quickly flood
Method comprises the following steps successively:
A. DEM border grid is extracted；
B. the DEM border grid that step A is extracted is inserted queue；
C. whether the queue in detecting step B is empty, if detection is not empty, then enters step D, if
It is detected as sky, then enters step F；
D. the minimum grid in step B queue is taken, subsequently into step E；
E. search for the upstream region of minimum grid, update inner boundary and return step C；
F. whether detection inner boundary is empty, if detection is not empty, then enters step G, if be detected as
Sky, then enter step J；
G. the minimum grid of inner boundary in step F is taken；
H. fill depression and record scope and the outlet in depression；
I. the upstream region in search step H record depression, updates inner boundary and returns F step；
J. depression detection terminates.
Electrical network facilities heavy rain methods of risk assessment the most according to claim 2, it is characterised in that: step E and
Search upstream region in step I comprises the following steps the most successively:
A. eight neighborhood grid of current mesh are searched for；
B. one of them in eight neighborhood grid in selecting step a；
C. judge that the neighborhood grid chosen in step b is the most processed, if it is determined that process, then enter
Step d, if it is determined that untreated, then enter step e；
D. judge that step b neighborhood is the most processed, if it is determined that processed, then enter step g, as
Fruit is judged as untreated, then return step b；
E. judge whether the neighborhood grid chosen in step b is less than current mesh, if the judgment is Yes, then
Enter step d, if the judgment is No, then enter step f；
F. neighborhood grid added priority query and be labeled as processed, and entering step d；
G. judge whether queue is empty, if it is determined that be not empty, then enter step h, if it is determined that empty,
Then enter step i；
H. take elevation minimum grid in queue, set current mesh, and return step a；
I. go up outbound search to complete.
Electrical network facilities heavy rain methods of risk assessment the most according to claim 2, it is characterised in that: in step H
Fill depression to comprise the following steps successively:
A). depression outlet is arranged to current mesh；
B). search step A) in eight neighborhood grid of current mesh；
C). selecting step B) in one of them in eight neighborhood grid；
D). judge step C) in the neighborhood grid chosen the most processed, if it is determined that process, then enter
Step E), if it is determined that untreated, then enter step F)；
E). judge step C) in neighborhood lattice the most processed, if it is determined that processed, then enter step
H), if it is determined that untreated, then return step C)；
F). judge neighborhood≤current mesh situation, if the judgment is Yes, then enter step G), if it is determined that
It is no, then enters step E)；
G). neighborhood grid added fifo queue and is labeled as processed, and entering step E)；
H). judge whether queue is empty, if it is determined that be not empty, then enter step I), if it is determined that empty,
Then enter step J)；
I). choose first grid of queue and be set as current mesh, and returning step B)；
J). depression has been filled.
Electrical network facilities heavy rain methods of risk assessment the most according to claim 1, it is characterised in that: step b)
In hazard-affected body vulnerability by czt=Su/Vr, Ht >=Hr, wherein: Su is current accumulated value, Vr
For critical reservoir storage, Ht is theoretical maximum depth of accumulated water, and Hr is the maximum depth of accumulated water that can bear.
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