CN103646157A - Method for evaluating transmission line fault caused by rainstorm - Google Patents
Method for evaluating transmission line fault caused by rainstorm Download PDFInfo
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- CN103646157A CN103646157A CN201310380170.2A CN201310380170A CN103646157A CN 103646157 A CN103646157 A CN 103646157A CN 201310380170 A CN201310380170 A CN 201310380170A CN 103646157 A CN103646157 A CN 103646157A
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Abstract
The invention discloses a method for evaluating a transmission line fault caused by rainstorm and belongs to the technical field of power systems and automation of the power systems. According to the mechanism that the rainstorm causes the transmission line fault, an accurate line fault probability model is established, and internal and external factors relevant with the transmission line fault are correctly reflected, so that evaluation on the transmission line fault caused by the rainstorm does not depend on accumulated historical data, pole tower stress and strength calculation are also avoided, various relevant factors are fully considered, and the method has good adaptability. By adopting the method, transmission line risk management is pushed greatly, and the capacity of defending the power systems against external disasters is enhanced.
Description
Technical field
The invention belongs to Power System and its Automation technical field, the present invention relates to more precisely a kind of method that heavy rain causes transmission line malfunction probability of assessing.
Background technology
Because China's primary energy and economic development are extremely uneven in areal distribution, primary energy mainly concentrates on west area as coal, water resources etc., and economic development area concentration is faster in East Coastal provinces and cities, in macroscopic view, determined that " national network, transferring electricity from the west to the east, north and south supply mutually " becomes China's basic orientation of interior most optimum distribution of resources on a large scale.Cause on national network cover high height above sea level, topographic and geologic complex area, the safe and reliable operation of transmission line of electricity is closely related with meteorology, geologic media etc. more.First, due to factor restrictions such as circuit corridor resource and geographical environments, transmission line of electricity is erected at high and steep mountains more, is very easily subject to the impact of weather environment; Secondly, global environment worsens in recent years, and the disasters such as storm flood and landslide, rubble flow happen occasionally, have sudden strong, have a very wide distribution, the feature such as the oncoming force is violent, very large to electric power facility harm; In addition, electric system at present mainly relies on personnel's line walking to find to the generation of the disasters such as landslide, rubble flow, and mainly concentrate on afterwards and sum up, effectively the impact of the disasters such as landslide, rubble flow is defendd in early warning, differ greatly with electrical network automatic intelligent target, cannot meet the new demand of strong intelligent grid to the management of transmission line of electricity operation risk.Therefore, be necessary to study the impact of the outside disasters such as heavy rain on transmission line of electricity, assessment line fault probability, strengthens the weather environment risk management of transmission line of electricity, for dispatching of power netwoks moves the support that provides the necessary technical.
The assessment that causes transmission line malfunction probability for heavy rain relates to circuit inside and outside various factors.Correlative factor is numerous and the mechanism of action is complicated to each other, several factors cannot accurately determine that (as factors such as geology landform) even cannot determine (as intensity of the basis of shaft tower, each member etc.) approx, thereby be difficult to analytic method, as by the method for real-time shaft tower force analysis line fault probability, be difficult to be applied to actual transmission line malfunction probability assessment.
Approximating method based on historical data, too depend on historical data and accuracy thereof, and can not embody the mechanism that heavy rain causes line fault, and be difficult at present obtain reliable historical data, so the method cannot be applied to Practical Project within considerable time.
Fu embraces uncut jade affects > > (geographical journal in < < landform and sea level elevation to precipitation, 1992, 47 (7): 302-314.), Liao Fei, Hong Yanchao He Zheng state light affects research overview > > (Meteorological Science And Technology < < Terrain on Precipitation, 2007, 35 (3): 309-316.), Chen Ming, Fu embraces the people such as uncut jade and Yu Qiang affects > > (geographical journal at < < Topography On Storm Rainfall, 1995, 50 (3): 256-263.) introduced sea level elevation, the impact of the factors such as topography and geomorphology on rainfall amount.Kuang Lehong has provided the stability factor computing method of bulk solids material in < < Regional Heavy Rain rubble flow prediction methods research > > (Hunan: Central South University, 2006.).
In recent years, frequency and degree that disaster occurs progressively increase and strengthen, threat to electricity net safety stable is increasing, but does not also meet the probability quantification assessment technology of safety on line analysis and early warning requirement at present, is therefore badly in need of carrying out the research and development of correlation technique.
Summary of the invention
The object of the invention is: in order to overcome above-mentioned the deficiencies in the prior art, the invention provides and a kind ofly can consider various correlative factors, embody the mechanism that heavy rain causes transmission line malfunction, realize the quantitative estimation method that heavy rain causes line fault probability.
Specifically, the present invention adopts following technical scheme to realize, and comprises the following steps:
1) in control center, gather real-time Rainstorm Forecast and live state information, general forecast and live state information, real-time electrical network work information;
2) according to circuit corridor geographic entity and surrounding enviroment feature by line sectionalizing, the circuit with identical geographic entity and surrounding enviroment feature divides one section into, and using in domatic lower exit place and the shaft tower in water channel exit, mountain valley as the shaft tower that affected by heavy rain;
3) according to the real-time detection data of radar Doppler, carry out linear extrapolation, rainfall scope and the rainfall intensity of following period of forecast, obtain in every section of circuit, being subject in forecasting period the rainfall intensity in the shaft tower region of living in that heavy rain affects;
4) to every section of circuit, the impact on rainfall intensity according to line facility present position geographic entity and surrounding enviroment feature, on revised by the rainfall intensity in the shaft tower region of living in that heavy rain affects, obtain in every section of circuit, being subject in revised forecasting period the rainfall intensity I in the shaft tower region of living in that heavy rain affects;
5) to every section of circuit, the dynamic variable that evaluates calculation is required, described dynamic variable comprises the stability factor K of the bulk solids material that is subject to the shaft tower region of living in that heavy rain affects in the average height of run-off h that is subject to the shaft tower region of living in that heavy rain affects in the effective precipitation r that is subject to the shaft tower region of living in that heavy rain affects in forecasting period in every section of circuit, forecasting period in every section of circuit, forecasting period in every section of circuit;
Calculated as follows by the effective precipitation r in the shaft tower region of living in that heavy rain affects:
r＝r
a+r
z+r
s
Wherein, r
afor being subject to the effective precipitation in indirect early stage in shaft tower region of living in that heavy rain affects, r in every section of circuit in forecasting period
zfor being subject to the effective precipitation in direct early stage in shaft tower region of living in that heavy rain affects, r in every section of circuit in forecasting period
sfor being subject to the short duration raininess in the shaft tower region of living in that heavy rain affects in every section of circuit in forecasting period; r
acalculate as follows:
R
ikrepresent i
krainfall amount a few days ago; β
afor characterizing the attenuation coefficient of Rock And Soil to the retentivity of rainwater, β
a≤ 1.0; n
1total number of days for rainfall in early stage;
Calculated in the following manner by the average height of run-off h in the shaft tower region of living in that heavy rain affects:
For the shaft tower in domatic lower exit place, the computing formula of the average height of run-off h in its region of living in is:
Wherein, c
dfor the slope concentration coefficient in each shaft tower region of living in domatic lower exit place in every section of circuit, t
dfor the rainwash time in each shaft tower region of living in domatic lower exit place in every section of circuit in forecasting period;
For the shaft tower in water channel exit, mountain valley, the computing formula of the average height of run-off h in its region of living in is:
h＝λIt
c
Wherein, λ is the coefficient that confluxes of the mountain valley water channel in the shaft tower region of living in water channel exit, mountain valley everywhere in every section of circuit, t
cfor in every section of circuit in forecasting period everywhere in the earth's surface inlet time in the shaft tower region of living in water channel exit, mountain valley;
In every section of circuit, be subject in described forecasting period the stability factor K of bulk solids material in the shaft tower region of living in that heavy rain affects by the stability factor calculated with mathematical model of bulk solids material:
Wherein, φ is the internal friction of bulk solids material, and θ is inclination angle, slope, ρ
satfor the density of bulk solids material after full water, ρ
mfor the density of silt carrying flow, ρ
sfor solid bulk materials density, ρ is water-mass density, and g is acceleration of gravity, and c is cohesion, and H is saturated bulk solids material thickness, and h is the average height of run-off that is subject to the shaft tower region of living in that heavy rain affects in the above-mentioned forecasting period calculating in every section of circuit; Wherein, the pass between c and φ is:
c＝0.04w
-0.95
Wherein, w is water content for relaxed matter;
6), to every section of circuit, in every section of circuit, be subject to as follows the mountain torrents strength factor E in the shaft tower region of living in that heavy rain affects in the CALCULATING PREDICTION period
m:
E
m＝sr/(r
Bβ)
Wherein, s is the topography and geomorphology coefficient that is subject to the shaft tower region of living in that heavy rain affects in every section of circuit, r
bfor benchmark rainfall value, β are the shaft tower safety coefficient that is subject to the shaft tower that heavy rain affects in every section of circuit;
Then, in every section of circuit, be subject to according to weather report the mountain torrents strength factor E in the shaft tower region of living in that heavy rain affects in the period
m, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects in every section of circuit, the probability that utilizes fuzzy mathematical model to calculate to be subject in every section of circuit shaft tower that heavy rain affects to break down under mountain flood, finally calculates the probability of malfunction p of whole piece circuit under mountain flood according to independent event new probability formula
1;
In this step, the process of establishing of fuzzy mathematical model is: first, and on being subject to the mountain torrents strength factor E of the shaft tower region that heavy rain affects in every section of circuit
mcarry out Fuzzy processing with the shaft tower the weakness α of the shaft tower that affected by heavy rain, set up respectively Triangleshape grade of membership function, calculate mountain torrents strength factor E
mdegree of membership with shaft tower the weakness α; Then, according to measured data and historical data, set up obfuscation rule, by weighted average method de-fuzzy, obtain the probability that shaft tower breaks down under mountain flood; Finally according to measured data, to mountain torrents strength factor E
mrevise with membership function and the fuzzy rule of shaft tower the weakness α, obtain final fuzzy data model;
7), to every section of circuit, in every section of circuit, be subject to as follows the landslide strength factor E in the shaft tower region of living in that heavy rain affects in the CALCULATING PREDICTION period
l:
E
l＝sc
1r/(r
Bβ)
Wherein, c
1for being subject to the geologic media coefficient in the shaft tower region of living in that heavy rain affects in every section of circuit;
Then, in every section of circuit, be subject to according to weather report the landslide strength factor E in the shaft tower region of living in that heavy rain affects in the period
l, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects in every section of circuit, the probability that utilizes fuzzy mathematical model to calculate to be subject in every section of circuit shaft tower that heavy rain affects to break down under landslide disaster, finally calculates the probability of malfunction p of whole piece circuit under landslide disaster according to independent event new probability formula
2;
In this step, the process of establishing of fuzzy mathematical model is: first, and on being subject to the landslide strength factor E of the shaft tower region that heavy rain affects in every section of circuit
lcarry out Fuzzy processing with the shaft tower the weakness α of the shaft tower that affected by heavy rain, set up respectively Triangleshape grade of membership function, calculate landslide strength factor E
ldegree of membership with shaft tower the weakness α; Then, according to measured data and historical data, set up obfuscation rule, by weighted average method de-fuzzy, obtain the probability that shaft tower breaks down under landslide disaster; Finally according to measured data, to landslide strength factor E
lrevise with membership function and the fuzzy rule of shaft tower the weakness α, obtain final fuzzy data model;
8), to every section of circuit, in every section of circuit, be subject to as follows the rubble flow strength factor E in the shaft tower region of living in that heavy rain affects in the CALCULATING PREDICTION period
w:
E
w＝Kr/(r
Bβ)
Then, in every section of circuit, be subject to according to weather report the rubble flow strength factor E in the shaft tower region of living in that heavy rain affects in the period
w, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects in every section of circuit, the probability that utilizes fuzzy mathematical model to calculate to be subject in every section of circuit shaft tower that heavy rain affects to break down under mud-stone flow disaster, finally calculates the probability of malfunction p of whole piece circuit under mud-stone flow disaster according to independent event new probability formula
3;
In this step, the process of establishing of fuzzy mathematical model is: first, and on being subject to the rubble flow strength factor E of the shaft tower region that heavy rain affects in every section of circuit
wcarry out Fuzzy processing with the shaft tower the weakness α of the shaft tower that affected by heavy rain, set up respectively Triangleshape grade of membership function, calculate rubble flow strength factor E
wdegree of membership with shaft tower the weakness α; Then, according to measured data and historical data, set up obfuscation rule, by weighted average method de-fuzzy, obtain the probability that shaft tower breaks down under mud-stone flow disaster; Finally according to measured data, to rubble flow strength factor E
wrevise with membership function and the fuzzy rule of shaft tower the weakness α, obtain final fuzzy data model;
9) calculate as follows the total probability of malfunction p of whole piece circuit:
p＝1-(1-p
1)(1-p
2)(1-p
3)
Finally, will in the total probability of malfunction result of calculation access blackout defense system of whole piece circuit, fault being carried out to risk assessment, is that wide area measurement is analyzed Protection control system screening anticipation risk equipment collection according to risk evaluation result.
Technique scheme is further characterized in that: in described step 5), and β
avalue be 0.8.
Technique scheme is further characterized in that: in described step 5), for general heavy rain, n
1value is 20 days; For torrential rain, n
1value is 10 days.
Technique scheme is further characterized in that: described step 6), 7), 8) in, calculate as follows the shaft tower the weakness α that is subject to the shaft tower that heavy rain affects in every section of circuit:
α=dmb
Wherein, the line span coefficient that d is every section of circuit, m is the shaft tower genre modulus that is subject to the shaft tower that heavy rain affects in every section of circuit, b is the pole and tower foundation strength factor that is subject to the shaft tower that heavy rain affects in every section of circuit.
Beneficial effect of the present invention is as follows: the present invention has realized the qualitative assessment that heavy rain causes transmission line malfunction probability, according to heavy rain, cause the mechanism of transmission line malfunction, set up line fault probability model accurately, correctly reflected the inside and outside portion factor relevant to transmission line malfunction, make heavy rain cause the accumulation that line fault probability assessment needn't depend on historical data, also avoided the stressed and intensity of shaft tower to calculate, take into full account the various correlative factors factor of precise quantification (particularly cannot), there is very strong adaptability.Therefore, this method has greatly promoted transmission line of electricity risk management, has improved the ability that outside disaster is defendd in electric system.
Accompanying drawing explanation
Fig. 1 is the process flow diagram of the inventive method.
Fig. 2 is the footpath flow diagram at domatic lower exit place.
Fig. 3 is the footpath flow diagram in water channel exit, mountain valley.
Embodiment
With reference to the accompanying drawings and in conjunction with example the present invention is described in further detail.
In Fig. 1, step 1 gathers real-time Rainstorm Forecast and live state information (rainfall intensity, duration etc.), general forecast and live state information, real-time electrical network work information in control center.
In Fig. 1 step 2 according to geographic entity and surrounding enviroment feature by line sectionalizing, the circuit with similar or identical geographic entity and surrounding enviroment feature is divided into one section, and using in domatic lower exit place and the shaft tower in water channel exit, mountain valley as the shaft tower that affected by heavy rain.
In Fig. 1, step 3, according to the real-time detection data of radar Doppler, is carried out linear extrapolation, rainfall intensity and the rainfall scope of following period of forecast.
1) rainfall scope forecast
Extrapolation to radar return, has adopted and echo barycenter has been made to the method for linear least square fitting in the position of adjacent moment.First according to radar return rate reflected value, calculate the position of barycenter in rectangular coordinate system, then obtain echo wave speed and the moving direction of barycenter, obtain rainfall scope.
2) rainfall intensity forecast
According to the actual measurement radar rainfall echo rate of front several periods, calculative determination one unit radar rainfall echo is the average rate of change of several periods in the past, and the rate of change using it as this radar return of next period, obtains thus the radar return rate of next period.Radar rainfall echo rate is carried out rainfall intensity estimation according to weather report, obtains in every section of circuit, being subject in forecasting period the rainfall intensity in the shaft tower region of living in that heavy rain affects.
Step 4 in Fig. 1, to every section of circuit, the impact on rainfall intensity according to line facility present position geographic entity and surrounding enviroment feature, on revised by the rainfall intensity in the shaft tower region of living in that heavy rain affects, obtain in every section of circuit, being subject in revised forecasting period the rainfall intensity I in the shaft tower region of living in that heavy rain affects.
Wherein, the pass of clinoform and rainfall intensity is: when the angle of prevailing wind direction and windward slope slope aspect is zero, landform is to the amplification of rainfall maximum (concrete numerical value is relevant with actual landform landforms); Terrain slope is between 30 °～60 ° time, and it is not clearly that landform changes the amplification of rainfall; Generally, the in the situation that of terrain slope homogeneous, windward slope rainfall amount is generally that the first increase with mountain height increases, to maximum rainfall height (maximum rainfall height generally appear at relative altitude 70%～80%), rainfall intensity is along with the increase of height reduces on the contrary, and what the precipitation intensity on mountain top may be than hillside pin is little; When mountain height is identical, the gradient generally appears at 45 ° of left and right to the maximum amplification of rainfall; When windward slope presents ladder, may there is two or more maximum rainfall height; Generally, the in the situation that of terrain slope homogeneous, always the precipitation intensity of leeward slope raises and increases with mountain height; And the gradient more approaches 45 °, change more obvious.
Step 5 in Fig. 1, to every section of circuit, the dynamic variable that evaluates calculation is required, described dynamic variable comprises the stability factor K of the bulk solids material that is subject to the shaft tower region of living in that heavy rain affects in the average height of run-off h that is subject to the shaft tower region of living in that heavy rain affects in the effective precipitation r that is subject to the shaft tower region of living in that heavy rain affects in forecasting period in every section of circuit, forecasting period in every section of circuit, forecasting period in every section of circuit.
The formation of landslide, rubble flow etc. is early stage effective precipitation and the coefficient result of short duration raininess.Generally speaking, rainfall duration in early stage is longer, and hillside rock mass contains water saturation, strength decreased, and slope body becomes unstable.Heavy rain after continuous rainfall is the dynamic condition that brings out this class disaster.Therefore, calculated as follows by the effective precipitation r in the shaft tower region of living in that heavy rain affects:
r＝r
a+r
z+r
s
Wherein, r
afor being subject to the effective precipitation in indirect early stage in shaft tower region of living in that heavy rain affects, r in every section of circuit in forecasting period
zfor being subject to the effective precipitation in direct early stage in shaft tower region of living in that heavy rain affects, r in every section of circuit in forecasting period
sfor being subject to the short duration raininess in the shaft tower region of living in that heavy rain affects in every section of circuit in forecasting period; r
acalculate as follows:
R
ikrepresent i
krainfall amount a few days ago; β
afor characterizing the attenuation coefficient of Rock And Soil to the retentivity of rainwater, by region Rock And Soil gross properties, determined β
a≤ 1.0, generally get 0.8; n
1for total number of days of rainfall in early stage, general heavy rain type value is 20 days, and torrential rain type value is 10 days.
Calculated in the following manner by the average height of run-off h in the shaft tower region of living in that heavy rain affects:
For the shaft tower in domatic lower exit place, its runoff situation as shown in Figure 2.While starting to produce runoff after rainwater landing, the current on hillside or sloping floor, are water layer as thin as a wafer at first, but through certain hour, can in slope, produce complete current, become steady flow.Suppose: 1) slope is straight line herein; 2) when forming slope runoff, top layer, slope bulk solids material is completely saturated; 3) within the forecast time period, storm intensity is constant in time.Therefore,, for the shaft tower in domatic lower exit place, the computing formula of the average height of run-off h in its region of living in is:
Wherein, c
dfor the slope concentration coefficient in each shaft tower region of living in domatic lower exit place in every section of circuit, as shown in table 1; t
dfor the rainwash time in each shaft tower region of living in domatic lower exit place in every section of circuit in forecasting period, current arrive shaft tower position required time from the hilltop, apparent distance length, terrain slope and ground coverage condition and determine desirable 5～15 minutes.
For the shaft tower in water channel exit, mountain valley, its runoff situation as shown in Figure 3.The active cross-section type of mountain valley water channel is complicated and changeable, and the impact for the ease of reflection current on shaft tower selects current height of run-off to assess.Suppose: 1), when forming raceway groove runoff, raceway groove side slope bulk solids material is completely saturated herein; 2) within the forecast time period, storm intensity is constant in time.Therefore,, for the shaft tower in water channel exit, mountain valley, the computing formula of the average height of run-off h in its region of living in is:
h＝λIt
c
Wherein, λ is the coefficient that confluxes of the mountain valley water channel in the shaft tower region of living in water channel exit, mountain valley everywhere in every section of circuit, also as shown in table 1; t
cfor in every section of circuit in forecasting period everywhere in the earth's surface inlet time in the shaft tower region of living in water channel exit, mountain valley, be that current are from raceway groove top, mountain valley to shaft tower position required time, apparent distance length, terrain slope and ground coverage condition and determine, reference value is 5～15 minutes.
Table 1 slope concentration coefficient and the mountain valley water channel coefficient that confluxes
In every section of circuit, be subject in described forecasting period the stability factor K of bulk solids material in the shaft tower region of living in that heavy rain affects by the stability factor calculated with mathematical model of bulk solids material:
Wherein, φ is the internal friction of bulk solids material, and θ is inclination angle, slope, ρ
satfor the density of bulk solids material after full water, the desirable 2.1g/cm of reference value
3, ρ
mfor the density of silt carrying flow, the desirable 1.1g/cm of reference value
3, ρ
sfor solid bulk materials density, the desirable 2.5g/cm of reference value
3, ρ is water-mass density, and g is acceleration of gravity, and c is cohesion, and H is saturated bulk solids material thickness, the desirable 0.4m of reference value, h is the average height of run-off that is subject to the shaft tower region of living in that heavy rain affects in the above-mentioned forecasting period calculating in every section of circuit; Wherein, the pass between c and φ is:
c＝0.04w
-0.95
Wherein, w is water content for relaxed matter;
In addition, other static information of using in the present invention comprises: the line span coefficient d of every section of circuit, get the ratio of actual span and reference value; In every section of circuit, be subject to the shaft tower genre modulus m of the shaft tower that heavy rain affects, take general shaft tower type as benchmark (value 1.0), the shaft tower that ability to bear is higher is heightened coefficient (1.0～2.0), and the weak shaft tower of ability to bear is turned down coefficient (0.5～1.0); The shaft tower safety coefficient β that is subject to the shaft tower that heavy rain affects in every section of circuit, span is 0～1.0; In every section of circuit, be subject to the geologic media coefficient c in the shaft tower region of living in that heavy rain affects
1, wherein, take general geology type environment as benchmark (value is 1.0), coefficient (1.0～2.0) is heightened in the location that the disasters such as landslide easily occur, coefficient (0.7～1.0) is turned down in the location that is difficult for the disasters such as generation landslide; In every section of circuit, be subject to the topography and geomorphology coefficient s in the shaft tower region of living in that heavy rain affects, take smooth relief as benchmark (value is 1.0), the landform such as physical features is precipitous, ravines and guillies criss-cross are heightened coefficient (1.0～1.8), and physical features is smooth, vegetation is luxuriant etc., and landform is turned down coefficient (0.5～1.0); In every section of circuit, be subject to the pole and tower foundation strength factor b of the shaft tower that heavy rain affects, take general shaft tower ground as benchmark (value 1.0), firmer ground is heightened coefficient (1.0～1.5), and weak ground is turned down coefficient (0.4～1.0); In every section of circuit, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects, α=dmb.
Step 6 in Fig. 1, to every section of circuit, is subject to the mountain torrents strength factor E in the shaft tower region of living in that heavy rain affects in the CALCULATING PREDICTION period as follows in every section of circuit
m:
E
m＝sr/(r
Bβ)
Wherein, s is the topography and geomorphology coefficient that is subject to the shaft tower region of living in that heavy rain affects in every section of circuit, r
bfor benchmark rainfall value (according to experience value, can revise according to actual conditions), β are the shaft tower safety coefficient that is subject to the shaft tower that heavy rain affects in every section of circuit.
Then, in every section of circuit, be subject to according to weather report the mountain torrents strength factor E in the shaft tower region of living in that heavy rain affects in the period
m, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects in every section of circuit, the probability that utilizes fuzzy mathematical model to calculate to be subject in every section of circuit shaft tower that heavy rain affects to break down under mountain flood, finally calculates the probability of malfunction p of whole piece circuit under mountain flood according to independent event new probability formula
1.
First, on being subject to the mountain torrents strength factor E of the shaft tower region that heavy rain affects in every section of circuit
mcarry out Fuzzy processing with the shaft tower the weakness α of the shaft tower that affected by heavy rain, set up respectively Triangleshape grade of membership function, calculate mountain torrents strength factor E
mdegree of membership with shaft tower the weakness α; Then, according to measured data and historical data, set up obfuscation rule, by weighted average method de-fuzzy, obtain the probability that shaft tower breaks down under mountain flood; Finally according to measured data, to mountain torrents strength factor E
mrevise with membership function and the fuzzy rule of shaft tower the weakness α, obtain final fuzzy data model, make probability of malfunction more accurate.
Step 7 in Fig. 1, to every section of circuit, is subject to the landslide strength factor E in the shaft tower region of living in that heavy rain affects in the CALCULATING PREDICTION period as follows in every section of circuit
l:
E
l＝sc
1r/(r
Bβ)
Wherein, c
1for being subject to the geologic media coefficient in the shaft tower region of living in that heavy rain affects in every section of circuit.
Then, in every section of circuit, be subject to according to weather report the landslide strength factor E in the shaft tower region of living in that heavy rain affects in the period
l, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects in every section of circuit, the probability that utilizes fuzzy mathematical model to calculate to be subject in every section of circuit shaft tower that heavy rain affects to break down under landslide disaster, finally calculates the probability of malfunction p of whole piece circuit under landslide disaster according to independent event new probability formula
2.
First, on being subject to the landslide strength factor E of the shaft tower region that heavy rain affects in every section of circuit
lcarry out Fuzzy processing with the shaft tower the weakness α of the shaft tower that affected by heavy rain, set up respectively Triangleshape grade of membership function, calculate landslide strength factor E
ldegree of membership with shaft tower the weakness α; Then, according to measured data and historical data, set up obfuscation rule, by weighted average method de-fuzzy, obtain the probability that shaft tower breaks down under landslide disaster; Finally according to measured data, to landslide strength factor E
lrevise with membership function and the fuzzy rule of shaft tower the weakness α, obtain final fuzzy data model, make probability of malfunction more accurate.
Step 8 in Fig. 1, to every section of circuit, is subject to the rubble flow strength factor E in the shaft tower region of living in that heavy rain affects in the CALCULATING PREDICTION period as follows in every section of circuit
w:
E
w＝Kr/(r
Bβ)
Then, in every section of circuit, be subject to according to weather report the rubble flow strength factor E in the shaft tower region of living in that heavy rain affects in the period
w, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects in every section of circuit, the probability that utilizes fuzzy mathematical model to calculate to be subject in every section of circuit shaft tower that heavy rain affects to break down under mud-stone flow disaster, finally calculates the probability of malfunction p of whole piece circuit under mud-stone flow disaster according to independent event new probability formula
3.
First, on being subject to the rubble flow strength factor E of the shaft tower region that heavy rain affects in every section of circuit
wcarry out Fuzzy processing with the shaft tower the weakness α of the shaft tower that affected by heavy rain, set up respectively Triangleshape grade of membership function, calculate rubble flow strength factor E
wdegree of membership with shaft tower the weakness α; Then, according to measured data and historical data, set up obfuscation rule, by weighted average method de-fuzzy, obtain the probability that shaft tower breaks down under mud-stone flow disaster; Finally according to measured data, to rubble flow strength factor E
wrevise with membership function and the fuzzy rule of shaft tower the weakness α, obtain final fuzzy data model, make probability of malfunction more accurate.
Step 9 in Fig. 1, calculate as follows the total probability of malfunction p of whole piece circuit:
p＝1-(1-p
1)(1-p
2)(1-p
3)
Finally, will in the total probability of malfunction result of calculation access blackout defense system of whole piece circuit, fault being carried out to risk assessment, is that wide area measurement is analyzed Protection control system screening anticipation risk equipment collection according to risk evaluation result.
Although the present invention with preferred embodiment openly as above, embodiment is not of the present invention for limiting.Without departing from the spirit and scope of the invention, any equivalence of doing changes or retouching, belongs to equally the present invention's protection domain.Therefore should to take the application's the content that claim was defined be standard to protection scope of the present invention.
Claims (4)
1. assessment heavy rain causes the method for transmission line malfunction probability, it is characterized in that, comprises the steps:
1) in control center, gather real-time Rainstorm Forecast and live state information, general forecast and live state information, real-time electrical network work information;
2) according to circuit corridor geographic entity and surrounding enviroment feature by line sectionalizing, the circuit with identical geographic entity and surrounding enviroment feature divides one section into, and using in domatic lower exit place and the shaft tower in water channel exit, mountain valley as the shaft tower that affected by heavy rain;
3) according to the real-time detection data of radar Doppler, carry out linear extrapolation, rainfall scope and the rainfall intensity of following period of forecast, obtain in every section of circuit, being subject in forecasting period the rainfall intensity in the shaft tower region of living in that heavy rain affects;
4) to every section of circuit, the impact on rainfall intensity according to line facility present position geographic entity and surrounding enviroment feature, on revised by the rainfall intensity in the shaft tower region of living in that heavy rain affects, obtain in every section of circuit, being subject in revised forecasting period the rainfall intensity I in the shaft tower region of living in that heavy rain affects;
5) to every section of circuit, the dynamic variable that evaluates calculation is required, described dynamic variable comprises the stability factor K of the bulk solids material that is subject to the shaft tower region of living in that heavy rain affects in the average height of run-off h that is subject to the shaft tower region of living in that heavy rain affects in the effective precipitation r that is subject to the shaft tower region of living in that heavy rain affects in forecasting period in every section of circuit, forecasting period in every section of circuit, forecasting period in every section of circuit;
Calculated as follows by the effective precipitation r in the shaft tower region of living in that heavy rain affects:
r＝r
a+r
z+r
s
Wherein, r
afor being subject to the effective precipitation in indirect early stage in shaft tower region of living in that heavy rain affects, r in every section of circuit in forecasting period
zfor being subject to the effective precipitation in direct early stage in shaft tower region of living in that heavy rain affects, r in every section of circuit in forecasting period
sfor being subject to the short duration raininess in the shaft tower region of living in that heavy rain affects in every section of circuit in forecasting period; r
acalculate as follows:
R
ikrepresent i
krainfall amount a few days ago; β
afor characterizing the attenuation coefficient of Rock And Soil to the retentivity of rainwater, β
a≤ 1.0; n
1total number of days for rainfall in early stage;
Calculated in the following manner by the average height of run-off h in the shaft tower region of living in that heavy rain affects:
For the shaft tower in domatic lower exit place, the computing formula of the average height of run-off h in its region of living in is:
Wherein, c
dfor the slope concentration coefficient in each shaft tower region of living in domatic lower exit place in every section of circuit, t
dfor the rainwash time in each shaft tower region of living in domatic lower exit place in every section of circuit in forecasting period;
For the shaft tower in water channel exit, mountain valley, the computing formula of the average height of run-off h in its region of living in is:
h＝λIt
c
Wherein, λ is the coefficient that confluxes of the mountain valley water channel in the shaft tower region of living in water channel exit, mountain valley everywhere in every section of circuit, t
cfor in every section of circuit in forecasting period everywhere in the earth's surface inlet time in the shaft tower region of living in water channel exit, mountain valley;
In every section of circuit, be subject in described forecasting period the stability factor K of bulk solids material in the shaft tower region of living in that heavy rain affects by the stability factor calculated with mathematical model of bulk solids material:
Wherein, φ is the internal friction of bulk solids material, and θ is inclination angle, slope, ρ
satfor the density of bulk solids material after full water, ρ
mfor the density of silt carrying flow, ρ
sfor solid bulk materials density, ρ is water-mass density, and g is acceleration of gravity, and c is cohesion, and H is saturated bulk solids material thickness, and h is the average height of run-off that is subject to the shaft tower region of living in that heavy rain affects in the above-mentioned forecasting period calculating in every section of circuit; Wherein, the pass between c and φ is:
c＝0.04w
-0.95
Wherein, w is water content for relaxed matter;
6), to every section of circuit, in every section of circuit, be subject to as follows the mountain torrents strength factor E in the shaft tower region of living in that heavy rain affects in the CALCULATING PREDICTION period
m:
E
m＝sr/(r
Bβ)
Wherein, s is the topography and geomorphology coefficient that is subject to the shaft tower region of living in that heavy rain affects in every section of circuit, r
bfor benchmark rainfall value, β are the shaft tower safety coefficient that is subject to the shaft tower that heavy rain affects in every section of circuit;
Then, in every section of circuit, be subject to according to weather report the mountain torrents strength factor E in the shaft tower region of living in that heavy rain affects in the period
m, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects in every section of circuit, the probability that utilizes fuzzy mathematical model to calculate to be subject in every section of circuit shaft tower that heavy rain affects to break down under mountain flood, finally calculates the probability of malfunction p of whole piece circuit under mountain flood according to independent event new probability formula
1;
In this step, the process of establishing of fuzzy mathematical model is: first, and on being subject to the mountain torrents strength factor E of the shaft tower region that heavy rain affects in every section of circuit
mcarry out Fuzzy processing with the shaft tower the weakness α of the shaft tower that affected by heavy rain, set up respectively Triangleshape grade of membership function, calculate mountain torrents strength factor E
mdegree of membership with shaft tower the weakness α; Then, according to measured data and historical data, set up obfuscation rule, by weighted average method de-fuzzy, obtain the probability that shaft tower breaks down under mountain flood; Finally according to measured data, to mountain torrents strength factor E
mrevise with membership function and the fuzzy rule of shaft tower the weakness α, obtain final fuzzy data model;
7), to every section of circuit, in every section of circuit, be subject to as follows the landslide strength factor E in the shaft tower region of living in that heavy rain affects in the CALCULATING PREDICTION period
l:
E
l＝sc
1r/(r
Bβ)
Wherein, c
1for being subject to the geologic media coefficient in the shaft tower region of living in that heavy rain affects in every section of circuit;
Then, in every section of circuit, be subject to according to weather report the landslide strength factor E in the shaft tower region of living in that heavy rain affects in the period
l, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects in every section of circuit, the probability that utilizes fuzzy mathematical model to calculate to be subject in every section of circuit shaft tower that heavy rain affects to break down under landslide disaster, finally calculates the probability of malfunction p of whole piece circuit under landslide disaster according to independent event new probability formula
2;
In this step, the process of establishing of fuzzy mathematical model is: first, and on being subject to the landslide strength factor E of the shaft tower region that heavy rain affects in every section of circuit
lcarry out Fuzzy processing with the shaft tower the weakness α of the shaft tower that affected by heavy rain, set up respectively Triangleshape grade of membership function, calculate landslide strength factor E
ldegree of membership with shaft tower the weakness α; Then, according to measured data and historical data, set up obfuscation rule, by weighted average method de-fuzzy, obtain the probability that shaft tower breaks down under landslide disaster; Finally according to measured data, to landslide strength factor E
lrevise with membership function and the fuzzy rule of shaft tower the weakness α, obtain final fuzzy data model;
8), to every section of circuit, in every section of circuit, be subject to as follows the rubble flow strength factor E in the shaft tower region of living in that heavy rain affects in the CALCULATING PREDICTION period
w:
E
w＝Kr/(r
Bβ)
Then, in every section of circuit, be subject to according to weather report the rubble flow strength factor E in the shaft tower region of living in that heavy rain affects in the period
w, be subject to the shaft tower the weakness α of the shaft tower that heavy rain affects in every section of circuit, the probability that utilizes fuzzy mathematical model to calculate to be subject in every section of circuit shaft tower that heavy rain affects to break down under mud-stone flow disaster, finally calculates the probability of malfunction p of whole piece circuit under mud-stone flow disaster according to independent event new probability formula
3;
In this step, the process of establishing of fuzzy mathematical model is: first, and on being subject to the rubble flow strength factor E of the shaft tower region that heavy rain affects in every section of circuit
wcarry out Fuzzy processing with the shaft tower the weakness α of the shaft tower that affected by heavy rain, set up respectively Triangleshape grade of membership function, calculate rubble flow strength factor E
wdegree of membership with shaft tower the weakness α; Then, according to measured data and historical data, set up obfuscation rule, by weighted average method de-fuzzy, obtain the probability that shaft tower breaks down under mud-stone flow disaster; Finally according to measured data, to rubble flow strength factor E
wrevise with membership function and the fuzzy rule of shaft tower the weakness α, obtain final fuzzy data model;
9) calculate as follows the total probability of malfunction p of whole piece circuit:
p＝1-(1-p
1)(1-p
2)(1-p
3)
Finally, will in the total probability of malfunction result of calculation access blackout defense system of whole piece circuit, fault being carried out to risk assessment, is that wide area measurement is analyzed Protection control system screening anticipation risk equipment collection according to risk evaluation result.
2. assessment heavy rain according to claim 1 causes the method for transmission line malfunction probability, it is characterized in that: in described step 5), and β
avalue be 0.8.
3. assessment heavy rain according to claim 1 causes the method for transmission line malfunction probability, it is characterized in that: in described step 5), for general heavy rain, n
1value is 20 days; For torrential rain, n
1value is 10 days.
4. assessment heavy rain according to claim 1 causes the method for transmission line malfunction probability, it is characterized in that: described step 6), 7), 8) in, calculate as follows the shaft tower the weakness α that is subject to the shaft tower that heavy rain affects in every section of circuit:
α=dmb
Wherein, the line span coefficient that d is every section of circuit, m is the shaft tower genre modulus that is subject to the shaft tower that heavy rain affects in every section of circuit, b is the pole and tower foundation strength factor that is subject to the shaft tower that heavy rain affects in every section of circuit.
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