CN106056851B - Electrical network facilities heavy rain method for early warning - Google Patents
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Abstract
The present invention relates to electrical network facilities heavy rain method for early warning, successively the following steps are included: Flood inducing factors and supporting body vulnerability analysis, urban waterlogging assessment, the setting of cause calamity Critical Rainfall, power grid disaster alarm；Advantages of the present invention: pass through Flood inducing factors and supporting body vulnerability analysis, urban waterlogging assessment, the method for early warning for causing the setting of calamity Critical Rainfall, power grid disaster alarm, it can solve in heavy rain overall process, the difficult point of Urban Stagnant Floods to give warning in advance, and according to early warning technology, it solves in heavy rain overall process, the problems such as flooded depth of Urban Stagnant Floods range, product, early warning critical value is simulated and determined, effectively predicts effectiveness factors and reduces property and economic loss caused by waterlogging.
Description
Technical field
The present invention relates to electrical network facilities heavy rain method for early warning.
Background technique
Urban waterlogging disaster is a kind of universal City Disasters type, it brings huge destruction, especially edge to city
Haicheng City, usually due to typhoon, heavy rain, tide and cause urban waterlogging, lead to the loss of casualties and fortune, it is adjoint
The continuous growth of China's economy, urbanization process accelerating, city size is in the continuous frequency expanded and extreme weather occurs
Increase, there are many planning of sewerage system, and oneself less goes up urbanization process, the drainpipe of quick urbanization process and low speed
The net transformation direct contradiction of speed is becoming increasingly acute, and original municipal drainage standard caused by the urbanization process of blindness is too low；City
City hardens area and increases, and permeability is deteriorated；Urbanization is unable to catch up in urban planning；Drainage System Construction lag, natural pool or city
The disappearance in city river, pumping equipment transformation in old town is difficult, and the spatial analysis application module of existing mathematics computing model and GIS come
Simulate waterlogging forming process and Disaster Assessment, when operation, is time-consuming more, and cannot give full play to the powerful spatial analysis functions of GIS,
It is particularly unsuited for carrying out the real-time simulation of effectiveness factors.In general, urban surface is divided into sub- watershed by urban waterlogging model,
But the city watershed of middle and small scale divides and is different from the division of large-scale catchments basin domain, for city watershed
Between boundary be difficult accurately to divide, it is mutually independent that hydrological watershed Partition Theory is built upon each watershed
On the basis of, there is certain discrepancy with practical situation.And this is a kind of black-box model, in urban waterlogging calculating process, only
The input of rainfall and the output of interior rushing result, can not show loss and the transmission process of water flow, it is difficult to consider to influence waterlogging
Many factors, such as landform morphosequent factor, land use factor etc., calculating data precision is more demanding, and data acquisition is difficult
Degree is big, therefore the model is relatively difficult in actual application.
Summary of the invention
The technical problem to be solved in the present invention is to provide electrical network facilities heavy rain method for early warning, solve existing urban waterlogging model
There is a problem of that time-consuming and data acquisition difficulty is big.
In order to solve the above-mentioned technical problem, the present invention is achieved by the following technical solutions: electrical network facilities heavy rain early warning
Method, successively the following steps are included:
A) Flood inducing factors and supporting body vulnerability analysis: different grades of critical rainfall intensity is extracted by rainfall, is faced
Boundary's rainfall, critical effective precipitation, submergence ratio and depth of the water submerging, and carrying is analyzed by submergence ratio and depth of the water submerging
Body vulnerability, critical rainfall intensity are the rainfall in the unit time, and critical excitation approaches are the rainfall in a period of time, critical
Effective precipitation is to reflect that current rainfall intensity and accumulation rainfall make slopes or bulk materials generation or there may be displacements
The equivalent rainfall of effect, critical effective precipitation Re=Ri+Rd+Ir × t, in which: Re is effective precipitation；Before Ri is indirect
Phase effective precipitation, the rainfall cumulant before being the same day；Rd is direct effective precipitation early period, is daily rainfall accumulation
Amount；Ir is rainfall intensity；T is the precipitation duration that raininess is Ir, supporting body include electric substation, switchgear house, cable run and equipment,
Capacitor, stand institute's building, distribution transformer, switchgear, ring network cabinet, on-pole switch equipment in distribution terminal equipment, bar；
B) urban waterlogging is assessed: analyzing urban waterlogging risk, urban waterlogging risk packet according to the rainfall that step a) is extracted
Include rainfall, runoff process, earth's surface Process of Confluence and pipe network Process of Confluence；
C) it causes the setting of calamity Critical Rainfall: seeking the Critical Rainfall of heavy rain electric network influencing by statistic inductive methods, and judgement is
It is no be more than critical excitation approaches, when Critical Rainfall be more than critical excitation approaches when, then enter step d), when Critical Rainfall be less than it is critical
When rainfall, as then return step a)；
D) power grid disaster alarm: by carrying out early warning processing to the region in step c) being more than critical excitation approaches and determining city
The warning grade of city's easily flood point.
Preferably, the rainfall in step b) by storm intensity state, storm intensity the following steps are included:
A rain data sampling: choosing catchment from existing Rainfall data, the rainfall duration of catchment is divided into 5
Minute, 10 minutes, 15 minutes, 20 minutes, 30 minutes, 45 minutes, 60 minutes, 90 minutes, 120 minutes, the rainfall of catchment
Return period counted by 0.25,0.33,0.5 year, 1 year, 2 years, 3 years, 5 years and 10 years；
B frequency analysis: according to the curve of frequency distribution of the step A rain data statistics storm intensity chosen；
C determines storm intensity parameter: lasting relationship by frequency-intensity-that step A and step B is obtained to estimate heavy rain
Intensive parameter, estimation method include least square method, simplex method, iterative method and genetic algorithm.
Preferably, the runoff process in step b) is calculated by runoff coefficient method, and urban area is divided by runoff coefficient method
As soon as several different ground surface types are simultaneously assigned to a coefficient to each ground surface type, then the coefficient is obtained multiplied by rainfall
Runoff yield.
Preferably, how the earth's surface Process of Confluence in step b) is after rainfall runoff generates in ground motion, and is finally led to
The process that inlet for stom water enters drainage pipeline networks is crossed, earth's surface Process of Confluence includes hydraulic model and hydrology model, hydraulic model
It establishes on the basis of microphysics law, the change in time and space of water flow, rain is solved according to the continuity equation and the equation of motion of water flow
Water flow movement in the water catchment area of the mouth of a river is overland flow process, and hydrology model is to establish rainfall input and basin rate of discharge process
Certain deterministic dependence.
Preferably, the one-dimensional Saint-venant Equations of control overland flow movement are as follows:
Wherein: x is water (flow) direction, and A is the area perpendicular to x-axis, and Q is the flow by section A, and y is the water of section A
Deep, vx is mean velocity in section, and S0 is ground line gradient, and Sf is the resistance gradient, and g is acceleration of gravity, and q1 is lower infiltration or rainfall, resistance
The power gradient:
OrOr
Wherein: f is Weisbach resistance coefficient；N is Manning roughness coefficien；Kn is conversion coefficient, using international unit
It is 1 when processed, then for 1.486 when using English unit；C is Chezy coefficient；R is hydraulic radius.
Preferably, hydrology model indicates that the concentration time of basin different zones is distributed by time-area diagram, unit arteries and veins
Punching responds
Wherein: (t- τ) is the concentration time, and dA is the unit area in basin.
Preferably, the rainfall runoff path in the earth's surface Process of Confluence in step b) is by center grid and eight adjacent neighbours
Domain grid composition, by neighborhood grid and center lattice net-shape at different gradient triangular facet, select the descending from center grid
The water (flow) direction of grid centered on the slope aspect of the maximum triangular facet of the gradient.
Preferably, rainwater converges into the concentration time of neighborhood grid from center grid
Wherein: nm is the Manning roughness coefficien of mesh region earth's surface, and g is the spacing of DEM grid, and I is net rainfall, Δ h
For the depth displacement for being directed toward the two adjacent grid connected by water flow, k is coefficient, the k=when water (flow) direction is along DEM grid direction
1, when water (flow) direction diagonally when k=2.
Preferably, the pipeline water flow in the pipe network Process of Confluence in step b) is
Qt+Δt=C0It+Δt+C1It+C2Qt；
Wherein: I is that the upstream of section enters water flow, and Q is that the upstream and downstream of section goes out water flow, and S is the reservoir storage in the section；
In above formula: LgFor duct length, it is long that L is characterized river, Q0For regime flow corresponding to a certain depth of water H, S0The bottom of for
Slope, B are water surface width, and C is velocity of wave, and C=η v, η are velocity of wave coefficient, and v is section average speed.
Preferably, the rainwater flow velocity in discharge process is calculated in step b) the following steps are included:
Step 1: the pipeline number of input root pipeline, and coupled upstream node is searched in the database；
Step 2: searching the upstream line being attached thereto according to upstream node, if so, then switch to step 3, if not having,
Then switch to step 4；
Step 3: upstream line is numbered as input conversion and step 1；
Step 4: the pipeline in step 1 is arranged to edge pipeline, upstream tube point is edge pipe point；
Step 5: the outflow Q of step 4 edge pipeline is calculated2=C1I1+C2I2+C3Q1；And the edge pipeline not on
Outbound pipeline, therefore the outflow at this time of edge pipeline is Q2=C1H1+C2H2+C3Q1, and calculated result is saved；
Step 6: the outflow of the edge pipeline calculated according to step 5 calculates the outflow Q of secondary side edge pipeline2=C1
(QL1+H1)+C2(QL2+H2)+C3Q1, and calculated result is saved；
Step 7: outflow of the step 6 until calculating root pipeline is repeated.
In conclusion advantages of the present invention: by Flood inducing factors and supporting body vulnerability analysis, urban waterlogging assessment, causing
The setting of calamity Critical Rainfall, power grid disaster alarm method for early warning, can solve in heavy rain overall process, Urban Stagnant Floods give warning in advance
Difficult point, and according to early warning technology, solves in heavy rain overall process, the flooded depth of Urban Stagnant Floods range, product, early warning critical value simulation and really
The problems such as determining effectively predicts effectiveness factors and reduces property and economic loss caused by waterlogging.
Detailed description of the invention
The present invention will be further explained below with reference to the attached drawings:
Fig. 1 is the structural schematic diagram of Heavy Rainfall Process line in Chicago of the present invention；
Fig. 2 is the structural schematic diagram of the non-flowing full round tube of the present invention.
Specific embodiment
Electrical network facilities heavy rain method for early warning, successively the following steps are included:
A) Flood inducing factors and supporting body vulnerability analysis: different grades of critical rainfall intensity is extracted by rainfall, is faced
Boundary's rainfall, critical effective precipitation, submergence ratio and depth of the water submerging, and carrying is analyzed by submergence ratio and depth of the water submerging
Body vulnerability, critical rainfall intensity are the rainfall in the unit time, and critical excitation approaches are the rainfall in a period of time, critical
Effective precipitation is to reflect that current rainfall intensity and accumulation rainfall make slopes or bulk materials generation or there may be displacements
The equivalent rainfall of effect, critical effective precipitation Re=Ri+Rd+Ir × t, in which: Re is effective precipitation；Before Ri is indirect
Phase effective precipitation, the rainfall cumulant before being the same day；Rd is direct effective precipitation early period, is daily rainfall accumulation
Amount；Ir is rainfall intensity；T is the precipitation duration that raininess is Ir, supporting body include electric substation, switchgear house, cable run and equipment,
Capacitor, stand institute's building, distribution transformer, switchgear, ring network cabinet, on-pole switch equipment in distribution terminal equipment, bar；
B) urban waterlogging is assessed: analyzing urban waterlogging risk, urban waterlogging risk packet according to the rainfall that step a) is extracted
Include rainfall, runoff process, earth's surface Process of Confluence and pipe network Process of Confluence；
C) it causes the setting of calamity Critical Rainfall: seeking the Critical Rainfall of heavy rain electric network influencing by statistic inductive methods, and judgement is
It is no be more than critical excitation approaches, when Critical Rainfall be more than critical excitation approaches when, then enter step d), when Critical Rainfall be less than it is critical
When rainfall, as then return step a)；
D) power grid disaster alarm: by carrying out early warning processing to the region in step c) being more than critical excitation approaches and determining city
The warning grade of city's easily flood point.
Preferably, the rainfall in step b) by storm intensity state, storm intensity the following steps are included:
A rain data sampling: choosing catchment from existing Rainfall data, the rainfall duration of catchment is divided into 5
Minute, 10 minutes, 15 minutes, 20 minutes, 30 minutes, 45 minutes, 60 minutes, 90 minutes, 120 minutes, the rainfall of catchment
Return period counted by 0.25,0.33,0.5 year, 1 year, 2 years, 3 years, 5 years and 10 years；
B frequency analysis: according to the curve of frequency distribution of the step A rain data statistics storm intensity chosen；
C determines storm intensity parameter: lasting relationship by frequency-intensity-that step A and step B is obtained to estimate heavy rain
Intensive parameter, estimation method include least square method, simplex method, iterative method and genetic algorithm.
When survey region does not have rainfall data or have insufficient data for one, a drop is indicated using average rainfall intensity
Rain, and rainfall intensity be it is constant, in actual rainfall, rainfall intensity is smaller when beginning, increase with time and gradually
Become larger, then becomes smaller again until rain stops, calculated accordingly by Heavy Rainfall Process line in Chicago shown in Fig. 1, in Fig. 1,
T1 is the rain peak moment, and rainfall intensity is maximum；T2 is quarter in rain stopping time；Tb indicates the period before peak, and ta indicates period, rain peak coefficient behind peak
γ=t1=t2 indicates rainfall with i (t) using having the heavy rain of identical mean intensity to generate rainfall graph with uniform rainfall pattern
Intensity changes with time, then is lasted for the mean intensity of the rainfall of td are as follows:
After introducing rain peak, the time difference at moment and rain peak moment before peak is indicated with tb, when indicating that the moment is with rain peak behind peak with ta
The time difference at quarter, then before peak the period instantaneous rainfall intensity are as follows:
The instantaneous rainfall intensity of period behind peak are as follows:
The piecewise function form of the instantaneous storm intensity of Chicago Heavy Rainfall Process line is
When model computational accuracy is of less demanding, the Annual distribution of rainfall intensity can be indicated using uniform rainfall pattern；If meter
When calculation required precision is higher, using Chicago Heavy Rainfall Process line, rain peak coefficient takes empirical value 0.4.
Runoff process in step b) is calculated by runoff coefficient method, and urban area is divided into several differences by runoff coefficient method
As soon as ground surface type and a coefficient is assigned to each ground surface type, then the coefficient has obtained runoff yield multiplied by rainfall,
Different runoff coefficients is chosen according to different floor types, the runoff coefficient of such as various roofings, concrete or bituminous pavement is
0.85~0.95；The runoff coefficient of the macadam pavement of boulder paving road surface or asphalt surface processing is 0.55~0.65；Gradation
The runoff coefficient of macadam pavement is 0.40~0.50；The runoff coefficient of laid dry masonry or macadam pavement is 0.35~0.40；Non- paving
The runoff coefficient for building soil surface is 0.25~0.35；The runoff coefficient in park or greenery patches is 0.10~0.20.
How earth's surface Process of Confluence in step b) is after rainfall runoff generates in ground motion, and eventually by inlet for stom water
Into the process of drainage pipeline networks, earth's surface Process of Confluence includes hydraulic model and hydrology model, and hydraulic model is established micro-
On the basis of seeing physical law, the change in time and space of water flow, inlet for stom water charge for remittance are solved according to the continuity equation and the equation of motion of water flow
Water flow movement in area is overland flow process, hydrology model be establish rainfall input and basin rate of discharge process certain really
Qualitative relationships.
Control the one-dimensional Saint-venant Equations of overland flow movement are as follows:
Wherein: x is water (flow) direction, and A is the area perpendicular to x-axis, and Q is the flow by section A, and y is the water of section A
Deep, vx is mean velocity in section, and S0 is ground line gradient, and Sf is the resistance gradient, and g is acceleration of gravity, and q1 is lower infiltration or rainfall, resistance
The power gradient:
OrOr
Wherein: f is Weisbach resistance coefficient；N is Manning roughness coefficien；Kn is conversion coefficient, using international unit
It is 1 when processed, then for 1.486 when using English unit；C is Chezy coefficient；R is hydraulic radius.
Hydrology model indicates that the concentration time of basin different zones is distributed by time-area diagram, and unit impulse response is
Wherein: (t- τ) is the concentration time, and dA is the unit area in basin.
Preferably, the rainfall runoff path in the earth's surface Process of Confluence in step b) is by center grid and eight adjacent neighbours
Domain grid composition, by neighborhood grid and center lattice net-shape at different gradient triangular facet, select the descending from center grid
The water (flow) direction of grid centered on the slope aspect of the maximum triangular facet of the gradient.
Rainwater converges into the concentration time of neighborhood grid from center grid
Wherein: nm is the Manning roughness coefficien of mesh region earth's surface, and g is the spacing of DEM grid, and I is net rainfall, Δ h
For the depth displacement for being directed toward the two adjacent grid connected by water flow, k is coefficient, the k=when water (flow) direction is along DEM grid direction
1, when water (flow) direction diagonally when k=2.
The pipeline water flow in pipe network Process of Confluence in step b) is
Qt+Δt=C0It+Δt+C1It+C2Qt；
Wherein: I is that the upstream of section enters water flow, and Q is that the upstream and downstream of section goes out water flow, and S is the reservoir storage in the section；
In above formula: LgFor duct length, it is long that L is characterized river, Q0For regime flow corresponding to a certain depth of water H, S0The bottom of for
Slope, B are water surface width, and C is velocity of wave, and C=η v, η are velocity of wave coefficient, and v is section average speed.
When pipe network bus dump is non-flowing full round tube, have as shown in Figure 2,
Wherein n is Manning's roughness coefficient, and D is pipe diameter,For central angle, arrange:
α and β are in formulaFunction.According to the research of Cen state equality people, α and the desirable constant of β: α=0.15, β=0.75,
Then have:
C is found out again0, C1And C2, flow Q after pipeline period Δ t can be found out by 4-2 formulat+Δt.It is disconnected that water is crossed in conjunction with pipeline
Region feature can must be corresponded to the depth of water h of calculation flow by Manning formula, it may be assumed that
Q is first passed through to find outAgain byCalculate the depth of water h of the section.It is asked from QUsing Newton iteration method, enableThen
IfUsing newton iteration formula, obtain
Finally:
The rainwater flow velocity in discharge process is calculated in step b) the following steps are included:
Step 1: the pipeline number of input root pipeline, and coupled upstream node is searched in the database；
Step 2: searching the upstream line being attached thereto according to upstream node, if so, then switch to step 3, if not having,
Then switch to step 4；
Step 3: upstream line is numbered as input conversion and step 1；
Step 4: the pipeline in step 1 is arranged to edge pipeline, upstream tube point is edge pipe point；
Step 5: the outflow Q of step 4 edge pipeline is calculated2=C1I1+C2I2+C3Q1；And the edge pipeline not on
Outbound pipeline, therefore the outflow at this time of edge pipeline is Q2=C1H1+C2H2+C3Q1, and calculated result is saved；
Step 6: the outflow of the edge pipeline calculated according to step 5 calculates the outflow Q of secondary side edge pipeline2=C1
(QL1+H1)+C2(QL2+H2)+C3Q1, and calculated result is saved；
Step 7: outflow of the step 6 until calculating root pipeline is repeated.
In addition to above preferred embodiment, there are other embodiments of the invention, and those skilled in the art can be according to this
Invention makes various changes and modifications, and as long as it does not depart from the spirit of the invention, should belong to appended claims of the present invention and determines
The range of justice.
Claims (7)
1. electrical network facilities heavy rain method for early warning, it is characterised in that: successively the following steps are included:
A) different grades of critical rainfall intensity, critical drop Flood inducing factors and supporting body vulnerability analysis: are extracted by rainfall
Rainfall, critical effective precipitation, submergence ratio and depth of the water submerging, and it is easy by submergence ratio and depth of the water submerging to analyze supporting body
Damage property, critical rainfall intensity are the rainfall in the unit time, and critical excitation approaches are the rainfall in a period of time, critical effective
Rainfall is to reflect that current rainfall intensity and accumulation rainfall make slopes or bulk materials generation or there may be displacement effects
Equivalent rainfall, critical effective precipitation Re=Ri+Rd+Ir × t, in which: Re is effective precipitation；Ri is to have indirect early period
Imitate rainfall, the rainfall cumulant before being the same day；Rd is direct effective precipitation early period, is daily rainfall cumulant；Ir
For rainfall intensity；T is the precipitation duration that raininess is Ir, and supporting body includes electric substation, switchgear house, cable run and equipment, distribution
Capacitor, stand institute's building, distribution transformer, switchgear, ring network cabinet, on-pole switch equipment on terminal device, bar；
B) urban waterlogging is assessed: analyzing urban waterlogging risk according to the rainfall that step a) is extracted, urban waterlogging risk includes drop
Rain process, runoff process, earth's surface Process of Confluence and pipe network Process of Confluence；
C) it causes the setting of calamity Critical Rainfall: seeking the Critical Rainfall of heavy rain electric network influencing by statistic inductive methods, and judge whether to surpass
Critical excitation approaches are crossed, when Critical Rainfall is more than critical excitation approaches, are then entered step d), when Critical Rainfall is less than critical rainfall
When amount, as then return step a)；
D) power grid disaster alarm: by carrying out early warning processing to the region in step c) being more than critical excitation approaches and determining that city is easy
The warning grade of flood point；
Rainfall in step b) is stated by storm intensity, storm intensity the following steps are included:
A rain data sampling: choosing catchment from existing Rainfall data, be divided into the rainfall duration of catchment 5 minutes,
10 minutes, 15 minutes, 20 minutes, 30 minutes, 45 minutes, 60 minutes, 90 minutes, 120 minutes, the repetition period of rainfall of catchment
It was counted by 0.25,0.33,0.5 year, 1 year, 2 years, 3 years, 5 years and 10 years；
B frequency analysis: according to the curve of frequency distribution of the step A rain data statistics storm intensity chosen；
C determines storm intensity parameter: lasting relationship by frequency-intensity-that step A and step B is obtained to estimate storm intensity
Parameter, estimation method include least square method, simplex method, iterative method and genetic algorithm；Pipe network Process of Confluence in step b)
In pipeline water flow be
Qt+Δt=C0It+Δt+C1It+C2Qt；
Wherein: I is that the upstream of section enters water flow, and Q is that the upstream and downstream of section goes out water flow, and S is the reservoir storage in the section；
In above formula: LgFor duct length, it is long that L is characterized river, Q0For regime flow corresponding to a certain depth of water H, S0For base slope, B
For water surface width, C is velocity of wave, and C=η v, η are velocity of wave coefficient, and v is section average speed；
The rainwater flow velocity in discharge process is calculated in step b) the following steps are included:
Step 1: the pipeline number of input root pipeline, and coupled upstream node is searched in the database；
Step 2: the upstream line being attached thereto is searched according to upstream node and, if not having, is turned if so, then switching to step 3
For step 4；
Step 3: upstream line is numbered as input conversion and step 1；
Step 4: the pipeline in step 1 is arranged to edge pipeline, upstream tube point is edge pipe point；
Step 5: the outflow Q of step 4 edge pipeline is calculated2=C1I1+C2I2+C3Q1；And the edge pipeline does not have upstream tube
Line, therefore the outflow at this time of edge pipeline is Q2=C1H1+C2H2+C3Q1, and calculated result is saved；
Step 6: the outflow of the edge pipeline calculated according to step 5 calculates the outflow Q of secondary side edge pipeline2=C1(QL1+H1)
+C2(QL2+H2)+C3Q1, and calculated result is saved；
Step 7: outflow of the step 6 until calculating root pipeline is repeated.
2. electrical network facilities heavy rain method for early warning according to claim 1, it is characterised in that: the runoff process in step b) is logical
Runoff coefficient method calculating is crossed, urban area is divided into several different ground surface types and to each ground surface type by runoff coefficient method
As soon as being assigned to a coefficient, then the coefficient has obtained runoff yield multiplied by rainfall.
3. electrical network facilities heavy rain method for early warning according to claim 1, it is characterised in that: the earth's surface in step b) was converged
How journey is after rainfall runoff generates in ground motion, and enters the process of drainage pipeline networks, earth's surface confluence eventually by inlet for stom water
Process includes hydraulic model and hydrology model, and hydraulic model is established on the basis of microphysics law, according to water flow
Continuity equation and the equation of motion solve the change in time and space of water flow, the water flow movement in inlet for stom water water catchment area is overland flow process,
Hydrology model is certain deterministic dependence for establishing rainfall input and basin rate of discharge process.
4. electrical network facilities heavy rain method for early warning according to claim 3, it is characterised in that: control overland flow moves one-dimensional
Saint-venant Equations are as follows:
Wherein: x is water (flow) direction, and A is the area perpendicular to x-axis, and Q is the flow by section A, and y is the depth of water of section A, vx
For mean velocity in section, S0 is ground line gradient, and Sf is the resistance gradient, and g is acceleration of gravity, and q1 is lower infiltration or rainfall, resistance slope
Degree:
OrOr
Wherein: f is Weisbach resistance coefficient；N is Manning roughness coefficien；Kn is conversion coefficient, when using the International System of Units
It is 1, then for 1.486 when using English unit；C is Chezy coefficient；R is hydraulic radius.
5. electrical network facilities heavy rain method for early warning according to claim 3, it is characterised in that: hydrology model passes through time face
Product figure indicates that the concentration time distribution of basin different zones, unit impulse response are
Wherein: (t- τ) is the concentration time, and dA is the unit area in basin.
6. electrical network facilities heavy rain method for early warning according to claim 3, it is characterised in that: the earth's surface in step b) was converged
Rainfall runoff path in journey is made of the adjacent neighborhood grid of center grid and eight, passes through neighborhood grid and center lattice net-shape
At different gradient triangular facet, selection is from grid centered on the slope aspect of the maximum triangular facet of downward grades of center grid
Water (flow) direction.
7. electrical network facilities heavy rain method for early warning according to claim 6, it is characterised in that: rainwater is converged into from center grid
The concentration time of neighborhood grid
Wherein: nm is the Manning roughness coefficien of mesh region earth's surface, and g is the spacing of DEM grid, and I is net rainfall, and Δ h is by water
Stream is directed toward the depth displacement of two adjacent grid of connection, and k is coefficient, and the k=1 when water (flow) direction is along DEM grid direction works as water
K=2 when flowing direction diagonally.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6947842B2 (en) * | 2003-01-06 | 2005-09-20 | User-Centric Enterprises, Inc. | Normalized and animated inundation maps |
| CN102902893A (en) * | 2012-10-29 | 2013-01-30 | 南京信息工程大学 | Method for calculating rainfall ponding depth of catchment area based on DEM (digital elevation model) |
| CN103646157A (en) * | 2013-08-28 | 2014-03-19 | 南京南瑞集团公司 | Method for evaluating transmission line fault caused by rainstorm |
| CN104851051A (en) * | 2014-12-08 | 2015-08-19 | 国家电网公司 | Dynamic-modification-combined storm rainfall fine alarming method for power grid zone |
-
2016
- 2016-05-13 CN CN201610322830.5A patent/CN106056851B/en active Active
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6947842B2 (en) * | 2003-01-06 | 2005-09-20 | User-Centric Enterprises, Inc. | Normalized and animated inundation maps |
| CN102902893A (en) * | 2012-10-29 | 2013-01-30 | 南京信息工程大学 | Method for calculating rainfall ponding depth of catchment area based on DEM (digital elevation model) |
| CN103646157A (en) * | 2013-08-28 | 2014-03-19 | 南京南瑞集团公司 | Method for evaluating transmission line fault caused by rainstorm |
| CN104851051A (en) * | 2014-12-08 | 2015-08-19 | 国家电网公司 | Dynamic-modification-combined storm rainfall fine alarming method for power grid zone |
Non-Patent Citations (5)
| Title |
|---|
| 《城市内涝积水量计算模型研究与应用》;温会;《城市内涝积水量计算模型研究与应用》;太原理工大学;20150608;正文第3，6，16，20，33，34，37页 |
| 《基于汇流时间方法的空间分布式水文模型原理》;谢华，都金康等;《武汉理工大学学报》;20051201;第27卷(第12期);正文第75-78页 |
| 《长历时暴雨强度公式的推求方法》;唐颖，周玉文等;《河北工业科技》;201409;正文第378-383页 |
| 《马歇京根法在雨水管道流量演算中的应用》;岑国平，沈进，范荣生;《西安理工大学学报》;1995;第11卷(第4期);正文第275-278页 |
| 暴雨影响电网安全的机理及其在线防御技术;常康，薛峰等;《陕西电力》;2014;第42卷(第12期);正文第6-10页 |
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