CN109472421A - A kind of power grid mountain fire sprawling method for early warning and device - Google Patents
A kind of power grid mountain fire sprawling method for early warning and device Download PDFInfo
- Publication number
- CN109472421A CN109472421A CN201811400733.9A CN201811400733A CN109472421A CN 109472421 A CN109472421 A CN 109472421A CN 201811400733 A CN201811400733 A CN 201811400733A CN 109472421 A CN109472421 A CN 109472421A
- Authority
- CN
- China
- Prior art keywords
- fire
- fire hazard
- power grid
- intensity
- flame spread
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 36
- 238000002485 combustion reaction Methods 0.000 claims abstract description 34
- 238000004364 calculation method Methods 0.000 claims description 17
- 238000001556 precipitation Methods 0.000 claims description 8
- 238000012937 correction Methods 0.000 claims description 4
- 230000004907 flux Effects 0.000 claims description 4
- 238000010438 heat treatment Methods 0.000 claims description 4
- 230000004044 response Effects 0.000 claims description 4
- 238000004590 computer program Methods 0.000 claims description 3
- 230000006399 behavior Effects 0.000 description 8
- 230000005611 electricity Effects 0.000 description 6
- 230000005540 biological transmission Effects 0.000 description 5
- 238000010168 coupling process Methods 0.000 description 5
- 238000005859 coupling reaction Methods 0.000 description 5
- 230000008878 coupling Effects 0.000 description 4
- 238000011160 research Methods 0.000 description 4
- 238000003860 storage Methods 0.000 description 3
- 230000008901 benefit Effects 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 238000011002 quantification Methods 0.000 description 2
- 230000015556 catabolic process Effects 0.000 description 1
- 238000012512 characterization method Methods 0.000 description 1
- 238000004891 communication Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000004069 differentiation Effects 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000005192 partition Methods 0.000 description 1
- 238000000513 principal component analysis Methods 0.000 description 1
- 238000012545 processing Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 239000002689 soil Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
- 238000012876 topography Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Public Health (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Water Supply & Treatment (AREA)
- Development Economics (AREA)
- Game Theory and Decision Science (AREA)
- Entrepreneurship & Innovation (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention provides a kind of power grid mountain fire sprawling method for early warning and devices, this method comprises: determining estimation range；The fire hazard factor for obtaining estimation range calculates fire hazard index according to the fire hazard factor and determines latent fire hazard grade；The weather forecast value that estimation range is obtained by WRF meteorologic model calculates flame spread rates, firewire intensity and the zonal combustion temperature of estimation range according to weather forecast value；Export and show fire hazard index, latent fire hazard grade, flame spread rates, firewire intensity and zonal combustion temperature.The present invention constructs the mountain fire sprawling Early-warning Model that fire hazard index, latent fire hazard grade, flame spread rates, firewire intensity and zonal combustion temperature are output, wherein, flame spread rates, firewire intensity and zonal combustion temperature are counted by the data of WRF meteorologic model, so that entire model more optimizes, it can precisely realize that mountain fire is forecast.
Description
Technical field
The present invention relates to anti-subtract of power grid disaster to control technical field more particularly to a kind of power grid mountain fire sprawling method for early warning and dress
It sets.
Background technique
With the rapid development of China's power grid, transmission line of electricity is continuous thousands of miles, need to often pass through forest zone and agriculture district, easily
By mountain fire influence and tripping of breaking down.Statistics display, the tripping of line fault caused by mountain fire has a power failure, stoppage in transit event be in by
The trend that year rises.Mountain fire not only causes great loss to people's production and living, but also to the safe and stable operation of power grid
Bring new challenge.Therefore, Early-warning Model is spread by carrying out mountain fire behavior quantification and mountain fire in power grid overlay area
Research can study and judge transmission line of electricity short-term operational reliability level after mountain fire occurs in advance, have a power failure for instructing scheduling decision to reduce
Risk promotes operation of power networks reliability and is of great significance.
For electric system mountain fire behavior prediction and study of warning, the transmission of electricity under mountain fire occurrence condition is focused primarily upon at present
Line tripping mechanism and mountain fire control measure research.And shortcoming is compared in power grid mountain fire behavior (such as mountain fire sprawling) research.Mountain fire hair
Raw probability also rests on the division of grade, and has done qualitative explanation to each grade, and there are no complete quantification.Although right
The problems such as mountain fire behavior has had certain research, big due to spatial resolution scale, the guidance to the anti-mountain fire O&M of power grid
Property is not strong.Mountain fire behavior relevant parameter and enough samples are observed and predicted in real time simultaneously as lacking, mountain fire prediction of behaviour model is still deposited
In biggish uncertainty, mountain fire forecasts that accuracy is lower.
Summary of the invention
The embodiment of the invention provides a kind of power grid mountain fire sprawling method for early warning and device, the gas based on WRF meteorologic model
Image data carries out statistical modeling, and output can precisely realize that mountain fire is forecast.
According to an aspect of the present invention, a kind of power grid mountain fire sprawling method for early warning is provided, comprising:
Determine estimation range；
The fire hazard factor for obtaining the estimation range calculates fire hazard index simultaneously according to the fire hazard factor
Determine latent fire hazard grade；
The weather forecast value that the estimation range is obtained by WRF meteorologic model calculates institute according to the weather forecast value
State flame spread rates, firewire intensity and the zonal combustion temperature of estimation range；
Export and show that the fire hazard index, the latent fire hazard grade, the flame spread rates, the firewire are strong
Degree and the zonal combustion temperature.
Preferably, the fire hazard factor includes: annual precipitation, the highest temperature, relative air humidity, wind speed and plant
By leaf area index.
Preferably, the calculation formula of the fire hazard index are as follows:
F=a0+a1·P+a2·T+a3·R+a4·V+a5·E
In formula, P is annual precipitation, and T is the highest temperature, and R is relative air humidity, and V is wind speed, and E is vegetation leaf area
Index.
Preferably, the flame spread rates include: the flame spread rates under calm condition and the fire spread under windy condition
Speed.
Preferably, the calculation formula of the flame spread rates under the calm condition are as follows:
In formula, IrFor response intensity, ξ is heat flux than coefficient, ρbFor combustible matter capacity density, ε is effective heating coefficient,
QigFor the heat that ignites in advance.
The calculation formula of flame spread rates under the windy condition are as follows:
v1=v0(1+ψv)
In formula, ψvFor wind speed correction factor.
Preferably, the calculation formula of the firewire intensity are as follows:
I=3.6Qwv
In formula, Q is the heat of combustible combustion, and w is the combustible mass consumed on unit area, and v is flame spread rates.
Preferably, the calculation formula of the zonal combustion temperature are as follows:
In formula, I is firewire intensity, and H is the height apart from ground.
According to another aspect of the present invention, a kind of power grid mountain fire sprawling prior-warning device is provided, comprising:
Determining module, for determining estimation range；
First computing module, for obtaining the fire hazard factor of the estimation range, according to the fire hazard factor
It calculates fire hazard index and determines latent fire hazard grade；
Second computing module, for obtaining the weather forecast value of the estimation range by WRF meteorologic model, according to described
Weather forecast value calculates the flame spread rates of the estimation range, firewire intensity and zonal combustion temperature；
Output module, for exporting and showing the fire hazard index, the latent fire hazard grade, fire spread speed
Degree, the firewire intensity and the zonal combustion temperature.
According to another aspect of the present invention, a kind of power grid mountain fire sprawling prior-warning device is provided, comprising: memory and coupling
It is connected to the processor of the memory；
The processor is configured to executing electricity as described above based on the instruction being stored in the memory devices
Net mountain fire spreads method for early warning.
According to another aspect of the present invention, a kind of computer-readable medium is provided, computer program is stored thereon with, it is special
Sign is that the program realizes above-described power grid mountain fire sprawling method for early warning when being executed by processor.
As can be seen from the above technical solutions, the embodiment of the present invention has the advantage that
The present invention provides a kind of power grid mountain fire sprawling method for early warning and devices, this method comprises: determining estimation range；It obtains
The fire hazard factor for taking estimation range calculates fire hazard index according to the fire hazard factor and determines latent fire hazard grade；
The weather forecast value that estimation range is obtained by WRF meteorologic model calculates the fire spread speed of estimation range according to weather forecast value
Degree, firewire intensity and zonal combustion temperature；Export and show fire hazard index, latent fire hazard grade, flame spread rates, firewire
Intensity and zonal combustion temperature.The present invention constructs fire hazard index, latent fire hazard grade, flame spread rates, firewire intensity
With the mountain fire sprawling Early-warning Model that zonal combustion temperature is output, wherein flame spread rates, firewire intensity and zonal combustion temperature
It is counted by the data of WRF meteorologic model, so that entire model more optimizes, can precisely realize that mountain fire is forecast.
Detailed description of the invention
In order to more clearly explain the embodiment of the invention or the technical proposal in the existing technology, to embodiment or will show below
There is attached drawing needed in technical description to be briefly described, it should be apparent that, the accompanying drawings in the following description is only this
Some embodiments of invention without any creative labor, may be used also for those of ordinary skill in the art
To obtain other attached drawings according to these attached drawings.
Fig. 1 is the flow diagram of one embodiment that a kind of power grid mountain fire provided by the invention spreads method for early warning；
Fig. 2 is the structural schematic diagram of one embodiment that a kind of power grid mountain fire provided by the invention spreads prior-warning device；
Fig. 3 is the scene of a fire schematic shapes under windy condition.
Specific embodiment
The embodiment of the invention provides a kind of power grid mountain fire sprawling method for early warning and device, the gas based on WRF meteorologic model
Image data carries out statistical modeling, and output can precisely realize that mountain fire is forecast.
In order to make the invention's purpose, features and advantages of the invention more obvious and easy to understand, below in conjunction with the present invention
Attached drawing in embodiment, technical scheme in the embodiment of the invention is clearly and completely described, it is clear that disclosed below
Embodiment be only a part of the embodiment of the present invention, and not all embodiment.Based on the embodiments of the present invention, this field
Those of ordinary skill's all other embodiment obtained without making creative work, belongs to protection of the present invention
Range.
Referring to Fig. 1, a kind of one embodiment of power grid mountain fire sprawling method for early warning provided by the invention；Include:
101, estimation range is determined；
102, the fire hazard factor for obtaining estimation range calculates fire hazard index and determination according to the fire hazard factor
Latent fire hazard grade；
103, the weather forecast value that estimation range is obtained by WRF meteorologic model calculates Target area according to weather forecast value
Flame spread rates, firewire intensity and the zonal combustion temperature in domain；
104, export and show fire hazard index, latent fire hazard grade, flame spread rates, firewire intensity and zonal combustion
Temperature.
The present invention constructs fire hazard index, latent fire hazard grade, flame spread rates, firewire intensity and zonal combustion temperature
Degree spreads Early-warning Model for the mountain fire of output, wherein flame spread rates, firewire intensity and zonal combustion temperature pass through WRF meteorology
The data of model are counted, so that entire model more optimizes, can precisely realize that mountain fire is forecast.
The above are a kind of one embodiment of power grid mountain fire sprawling method for early warning to mention below to carry out more specific description
For a kind of another embodiment of power grid mountain fire sprawling method for early warning, referring to Fig. 2, a kind of power grid mountain fire provided by the invention is climing
Prolong another embodiment of method for early warning, comprising:
201, estimation range is determined；
Around grid power transmission route distribution, it is divided into one or more regions vulnerable to mountain fire influence area.Needle
The region adjacent to different brackets transmission line of electricity can be with merging treatment, then, and determination needs to carry out early warning in numerous regions
Estimation range.
202, the fire hazard factor for obtaining estimation range calculates fire hazard index and determination according to the fire hazard factor
Latent fire hazard grade；
In the present embodiment, using Chinese T 639 or U.S.'s GFS Meteorological Products and station data, in conjunction with fire data and ground
Shape vegetation information establishes the multiple linear regression equations of fire generation using multiple-factor statistical analysis, will be various in the equation
Meteorological element, combustible and terrain factor carry out principal component analysis, calculate Factor Weight coefficient and weight ratio, select weight ratio most
Big preceding 5 factors are as the estimation range fire hazard factor, comprising: annual precipitation, the highest temperature, air are relatively wet
Degree, wind speed and vegetation leaf area index.
Further, the calculation formula of fire hazard index are as follows:
F=a0+a1·P+a2·T+a3·R+a4·V+a5·E
In formula, P is annual precipitation, and T is the highest temperature, and R is relative air humidity, and V is wind speed, and E is vegetation leaf area
Index.Wherein, ai(i=0,1,2,3,4,5) is regression coefficient, can be preset by testing in advance.According to F numerical values recited and sky
Between be distributed, mark off latent fire hazard grade using probability simulation and region clustering method:
1 region mountain fire latent fire hazard of table-warning grade criterion
203, the weather forecast value that estimation range is obtained by WRF meteorologic model calculates Target area according to weather forecast value
Flame spread rates, firewire intensity and the zonal combustion temperature in domain；
If mountain fire has occurred in latent fire hazard region in step 202, by combustible situation, thermal energy rate of release,
The influence of the factors such as landform and weather conditions, mountain fire behavior expression go out various sprawling features, such as: flame spread rates,
Fire intensity, flame height, ignition temperature etc..Flame spread rates refer to the mobile distance of unit time flame, flat with effective wind speed
Side is closely related；Fire spread direction is decided by wind direction.
In topography under flat and inflammable Vegetation condition, flame spread rates depend primarily on weather environment (especially wind field ring
Border).Weather forecast value (the relevant parameter in i.e. following flame spread rates formula of estimation range is obtained by WRF meteorologic model
Variable), substituting into following mountain fire sprawling empirical estimating formula can acquire or rate of propagation.Wherein, flame spread rates include: calm
Under the conditions of flame spread rates and the flame spread rates under windy condition.
Further, under calm condition, since rate of propagation is identical around for flame, scene of a fire shape can be approximately round
Shape, therefore the calculation formula of flame spread rates are as follows:
In formula, IrFor response intensity, ξ is heat flux than coefficient, ρbFor combustible matter capacity density, ε is effective heating coefficient,
QigFor the heat that ignites in advance.
Under windy condition, in the case where having wind, flame is maximum in downwind vertical spread speed, in upwind vertical spread
Speed is minimum, and scene of a fire shape is no longer rounded, and is formed using incendiary source as the ellipse of focus, sees Fig. 3, therefore flame spread rates
Calculation formula are as follows:
v1=v0(1+ψv)
In formula, ψvFor wind speed correction factor.In addition, in the case where known maximum mountain fire rate of propagation and excessive fire time,
It is assured that sprawling distance of the fire on downwind, may thereby determine that scene of a fire shape.
Further, the speed that heat discharges when combustible combustion is known as Forest Fire intensity, abbreviation fire intensity.Fire intensity is
The important symbol of mountain fire behavior can evaluate mountain fire to the breakdown strength of vegetation, earth's surface biological, soil etc. with this.And firewire intensity
Refer in the unit time, the heat generated on unit fire perimeter, can be used to characterization fire intensity.According to the fire of U.S. Byram
Line strength formula, firewire intensity is related with flame spread rates and combustible condition, i.e. the calculation formula of firewire intensity are as follows:
I=3.6Qwv
In formula, Q is the heat of combustible combustion, and w is the combustible mass consumed on unit area, and v is flame spread rates,
Flame spread rates i.e. under calm condition or windy condition.
Further, the calculation formula of zonal combustion temperature are as follows:
In formula, I is firewire intensity, and H is the height apart from ground.
204, export and show fire hazard index, latent fire hazard grade, flame spread rates, firewire intensity and zonal combustion
Temperature.
Fire hazard index, latent fire hazard grade, flame spread rates, firewire intensity and zonal combustion temperature are being obtained, i.e.,
It can be exported as the result of the Early-warning Model of the embodiment of the present invention, and carry out plot and display.
The present invention comprehensively considers meteorological element in region, combustible, landform, fire source variation characteristic, and its with history fire
Disaster loss statistic correlation determines fire hazard index and latent fire hazard region, and makes different latent fire hazard grades, it is ensured that mountain
The predictor and the Forecasting Object degree of association of fire spread Early-warning Model are higher, and quantitatively objectively embody true when mountain fire occurs
Real situation reduces the differentiation of subjectivity.
The present invention is atmosphere forcing field using 639 Meteorological Products of Chinese T, to drive high-resolution numerical weather model
(WRF), the weather forecast factor under microclimate environment is obtained, mountain fire sprawling speed is calculated in Combining with terrain feature and vegetation information
Degree, direction and fire intensity, have ensured the objectivity and accuracy of fire spread early warning product.
The present invention carries out numerical statistic Forecast model for magnanimity meteorology big data, for the calibration of implementation model, examines
The value of forecasting of forecast system carries out a large amount of Hindcast experiments and optimization, and the value of forecasting and precision of the Early-warning Model will be continuous
It improves.
It is the detailed description carried out to a kind of power grid mountain fire sprawling method for early warning provided by the invention above, it below will be to this
The structure and connection relationship for a kind of power grid mountain fire sprawling prior-warning device that invention provides are illustrated, referring to Fig. 2, the present invention mentions
A kind of one embodiment of the power grid mountain fire sprawling prior-warning device supplied, comprising:
Determining module 301, for determining estimation range；
First computing module 302 calculates fire according to the fire hazard factor for obtaining the fire hazard factor of estimation range
Calamity hazard index simultaneously determines latent fire hazard grade；
Second computing module 303, for obtaining the weather forecast value of estimation range by WRF meteorologic model, according to meteorology
Flame spread rates, firewire intensity and the zonal combustion temperature of predicted value calculating estimation range；
Output module 304, for exporting and showing that fire hazard index, latent fire hazard grade, flame spread rates, firewire are strong
Degree and zonal combustion temperature.
Further, the fire hazard factor includes: annual precipitation, the highest temperature, relative air humidity, wind speed and plant
By leaf area index.
Further, the calculation formula of fire hazard index are as follows:
F=a0+a1·P+a2·T+a3·R+a4·V+a5·E
In formula, P is annual precipitation, and T is the highest temperature, and R is relative air humidity, and V is wind speed, and E is vegetation leaf area
Index.
Further, flame spread rates include: the flame spread rates under calm condition and the fire spread under windy condition
Speed.
Further, the calculation formula of the flame spread rates under calm condition are as follows:
In formula, IrFor response intensity, ξ is heat flux than coefficient, ρbFor combustible matter capacity density, ε is effective heating coefficient,
QigFor the heat that ignites in advance.
The calculation formula of flame spread rates under windy condition are as follows:
v1=v0(1+ψv)
In formula, ψvFor wind speed correction factor.
Further, the calculation formula of firewire intensity are as follows:
I=3.6Qwv
In formula, Q is the heat of combustible combustion, and w is the combustible mass consumed on unit area, and v is flame spread rates.
Further, the calculation formula of zonal combustion temperature are as follows:
In formula, I is firewire intensity, and H is the height apart from ground.
Another embodiment of a kind of power grid mountain fire sprawling prior-warning device provided by the invention, comprising: memory and coupling
It is connected to the processor of the memory；
The processor is configured to executing electricity as described above based on the instruction being stored in the memory devices
Net mountain fire spreads method for early warning.
The invention further relates to a kind of computer-readable mediums, are stored thereon with computer program, which is characterized in that the program
Above-described power grid mountain fire sprawling method for early warning is realized when being executed by processor.
It is apparent to those skilled in the art that for convenience and simplicity of description, the system of foregoing description,
The specific work process of device and unit, can refer to corresponding processes in the foregoing method embodiment, and details are not described herein.
In several embodiments provided herein, it should be understood that disclosed system, device and method can be with
It realizes by another way.For example, the apparatus embodiments described above are merely exemplary, for example, the unit
It divides, only a kind of logical function partition, there may be another division manner in actual implementation, such as multiple units or components
It can be combined or can be integrated into another system, or some features can be ignored or not executed.Another point, it is shown or
The mutual coupling, direct-coupling or communication connection discussed can be through some interfaces, the indirect coupling of device or unit
It closes or communicates to connect, can be electrical property, mechanical or other forms.
The unit as illustrated by the separation member may or may not be physically separated, aobvious as unit
The component shown may or may not be physical unit, it can and it is in one place, or may be distributed over multiple
In network unit.It can select some or all of unit therein according to the actual needs to realize the mesh of this embodiment scheme
's.
It, can also be in addition, the functional units in various embodiments of the present invention may be integrated into one processing unit
It is that each unit physically exists alone, can also be integrated in one unit with two or more units.Above-mentioned integrated list
Member both can take the form of hardware realization, can also realize in the form of software functional units.
If the integrated unit is realized in the form of SFU software functional unit and sells or use as independent product
When, it can store in a computer readable storage medium.Based on this understanding, technical solution of the present invention is substantially
The all or part of the part that contributes to existing technology or the technical solution can be in the form of software products in other words
It embodies, which is stored in a storage medium, including some instructions are used so that a computer
Equipment (can be personal computer, server or the network equipment etc.) executes the complete of each embodiment the method for the present invention
Portion or part steps.And storage medium above-mentioned includes: USB flash disk, mobile hard disk, read-only memory (ROM, Read-Only
Memory), random access memory (RAM, Random Access Memory), magnetic or disk etc. are various can store journey
The medium of sequence code.
The above, the above embodiments are merely illustrative of the technical solutions of the present invention, rather than its limitations；Although referring to before
Stating embodiment, invention is explained in detail, those skilled in the art should understand that: it still can be to preceding
Technical solution documented by each embodiment is stated to modify or equivalent replacement of some of the technical features；And these
It modifies or replaces, the spirit and scope for technical solution of various embodiments of the present invention that it does not separate the essence of the corresponding technical solution.
Claims (10)
1. a kind of power grid mountain fire spreads method for early warning characterized by comprising
Determine estimation range；
The fire hazard factor for obtaining the estimation range calculates fire hazard index and determination according to the fire hazard factor
Latent fire hazard grade；
The weather forecast value that the estimation range is obtained by WRF meteorologic model calculates described pre- according to the weather forecast value
Survey flame spread rates, firewire intensity and the zonal combustion temperature in region；
Export and show the fire hazard index, the latent fire hazard grade, the flame spread rates, the firewire intensity and
The zonal combustion temperature.
2. power grid mountain fire according to claim 1 spreads method for early warning, which is characterized in that the fire hazard is because of attached bag
It includes: annual precipitation, the highest temperature, relative air humidity, wind speed and vegetation leaf area index.
3. power grid mountain fire according to claim 2 spreads method for early warning, which is characterized in that the meter of the fire hazard index
Calculate formula are as follows:
F=a0+a1·P+a2·T+a3·R+a4·V+a5·E
In formula, P is annual precipitation, and T is the highest temperature, and R is relative air humidity, and V is wind speed, and E is vegetation leaf area index.
4. power grid mountain fire according to claim 1 spreads method for early warning, which is characterized in that the flame spread rates include:
The flame spread rates under flame spread rates and windy condition under calm condition.
5. power grid mountain fire according to claim 4 spreads method for early warning, which is characterized in that the fire under the calm condition is climing
Prolong the calculation formula of speed are as follows:
In formula, IrFor response intensity, ξ is heat flux than coefficient, ρbFor combustible matter capacity density, ε is effective heating coefficient, QigFor
Ignite heat in advance.
The calculation formula of flame spread rates under the windy condition are as follows:
v1=v0(1+ψv)
In formula, ψvFor wind speed correction factor.
6. power grid mountain fire according to claim 5 spreads method for early warning, which is characterized in that the calculating of the firewire intensity is public
Formula are as follows:
I=3.6Qwv
In formula, Q is the heat of combustible combustion, and w is the combustible mass consumed on unit area, and v is flame spread rates.
7. power grid mountain fire according to claim 6 spreads method for early warning, which is characterized in that the meter of the zonal combustion temperature
Calculate formula are as follows:
In formula, I is firewire intensity, and H is the height apart from ground.
8. a kind of power grid mountain fire spreads prior-warning device characterized by comprising
Determining module, for determining estimation range；
First computing module is calculated for obtaining the fire hazard factor of the estimation range according to the fire hazard factor
Fire hazard index simultaneously determines latent fire hazard grade；
Second computing module, for obtaining the weather forecast value of the estimation range by WRF meteorologic model, according to the meteorology
Predicted value calculates the flame spread rates of the estimation range, firewire intensity and zonal combustion temperature；
Output module, for export and show the fire hazard index, the latent fire hazard grade, the flame spread rates,
The firewire intensity and the zonal combustion temperature.
9. a kind of power grid mountain fire spreads prior-warning device characterized by comprising memory, and it is coupled to the memory
Processor；
The processor is configured to being executed based on the instruction being stored in the memory devices as claim 1 to 7 is any
Power grid mountain fire described in one spreads method for early warning.
10. a kind of computer-readable medium, is stored thereon with computer program, which is characterized in that the program is executed by processor
Power grid mountain fire described in Shi Shixian claim 1 to 7 any one spreads method for early warning.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811400733.9A CN109472421A (en) | 2018-11-22 | 2018-11-22 | A kind of power grid mountain fire sprawling method for early warning and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811400733.9A CN109472421A (en) | 2018-11-22 | 2018-11-22 | A kind of power grid mountain fire sprawling method for early warning and device |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN109472421A true CN109472421A (en) | 2019-03-15 |
Family
ID=65673237
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811400733.9A Pending CN109472421A (en) | 2018-11-22 | 2018-11-22 | A kind of power grid mountain fire sprawling method for early warning and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109472421A (en) |
Cited By (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110163251A (en) * | 2019-04-15 | 2019-08-23 | 深圳市中电数通智慧安全科技股份有限公司 | A kind of Optimum Identification Method of fire hazard rating, device and terminal device |
| CN110210769A (en) * | 2019-06-06 | 2019-09-06 | 国网湖南省电力有限公司 | A kind of transmission line forest fire sprawling Risk Forecast Method and system |
| CN110634258A (en) * | 2019-08-19 | 2019-12-31 | 广西电网有限责任公司电力科学研究院 | Mountain fire identification method aiming at satellite monitoring mountain fire data of power transmission line |
| CN110969798A (en) * | 2019-11-26 | 2020-04-07 | 国网山西省电力公司电力科学研究院 | Power transmission line forest fire early warning method and system |
| CN111583567A (en) * | 2020-06-01 | 2020-08-25 | 江西憶源多媒体科技有限公司 | Forest fire prevention early warning method and device |
| CN112464819A (en) * | 2020-11-27 | 2021-03-09 | 清华大学 | Forest fire spreading data assimilation method and device based on unmanned aerial vehicle video |
| CN112817572A (en) * | 2019-11-15 | 2021-05-18 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | Power transmission line forest fire early warning method |
| CN113111518A (en) * | 2021-04-15 | 2021-07-13 | 应急管理部四川消防研究所 | Fire simulation processing method based on Internet of things |
| CN113137218A (en) * | 2020-01-16 | 2021-07-20 | 中国石油天然气股份有限公司 | Method and device for determining fire flooding front edge position of fire flooding gas injection well |
| CN113192282A (en) * | 2021-04-16 | 2021-07-30 | 南京玄甲物联科技有限公司 | Fire early warning system based on internet of things |
| CN113704960A (en) * | 2021-07-12 | 2021-11-26 | 国网河北省电力有限公司电力科学研究院 | Method for determining fire spreading speed of parallel double cables |
| CN117592789A (en) * | 2024-01-18 | 2024-02-23 | 山东金桥保安器材有限公司 | Power grid environment fire risk assessment method and equipment based on time sequence analysis |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104376510A (en) * | 2014-12-05 | 2015-02-25 | 国家电网公司 | Method of predicting and accessing level of wildfire-caused trip risk in power transmission lines |
| CN105787602A (en) * | 2016-03-16 | 2016-07-20 | 华北电力大学 | Power transmission line wildfire dynamic forecasting and early warning method based on sequential variation |
| CN107038833A (en) * | 2017-06-13 | 2017-08-11 | 广东电网有限责任公司电力科学研究院 | A kind of mountain fire fire secondary discriminating method of point and system |
-
2018
- 2018-11-22 CN CN201811400733.9A patent/CN109472421A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104376510A (en) * | 2014-12-05 | 2015-02-25 | 国家电网公司 | Method of predicting and accessing level of wildfire-caused trip risk in power transmission lines |
| CN105787602A (en) * | 2016-03-16 | 2016-07-20 | 华北电力大学 | Power transmission line wildfire dynamic forecasting and early warning method based on sequential variation |
| CN107038833A (en) * | 2017-06-13 | 2017-08-11 | 广东电网有限责任公司电力科学研究院 | A kind of mountain fire fire secondary discriminating method of point and system |
Non-Patent Citations (1)
| Title |
|---|
| 冯治学: "电网山火灾害风险评估模型研究", 《中国优秀硕士学位论文全文数据库》 * |
Cited By (16)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110163251A (en) * | 2019-04-15 | 2019-08-23 | 深圳市中电数通智慧安全科技股份有限公司 | A kind of Optimum Identification Method of fire hazard rating, device and terminal device |
| CN110210769A (en) * | 2019-06-06 | 2019-09-06 | 国网湖南省电力有限公司 | A kind of transmission line forest fire sprawling Risk Forecast Method and system |
| CN110634258A (en) * | 2019-08-19 | 2019-12-31 | 广西电网有限责任公司电力科学研究院 | Mountain fire identification method aiming at satellite monitoring mountain fire data of power transmission line |
| CN112817572A (en) * | 2019-11-15 | 2021-05-18 | 内蒙古电力(集团)有限责任公司内蒙古电力科学研究院分公司 | Power transmission line forest fire early warning method |
| CN110969798A (en) * | 2019-11-26 | 2020-04-07 | 国网山西省电力公司电力科学研究院 | Power transmission line forest fire early warning method and system |
| CN113137218A (en) * | 2020-01-16 | 2021-07-20 | 中国石油天然气股份有限公司 | Method and device for determining fire flooding front edge position of fire flooding gas injection well |
| CN113137218B (en) * | 2020-01-16 | 2023-05-26 | 中国石油天然气股份有限公司 | Method and device for determining position of fire flooding front edge of fire flooding gas injection well |
| CN111583567A (en) * | 2020-06-01 | 2020-08-25 | 江西憶源多媒体科技有限公司 | Forest fire prevention early warning method and device |
| CN112464819A (en) * | 2020-11-27 | 2021-03-09 | 清华大学 | Forest fire spreading data assimilation method and device based on unmanned aerial vehicle video |
| CN112464819B (en) * | 2020-11-27 | 2024-01-12 | 清华大学 | Forest fire spread data assimilation method and device based on unmanned aerial vehicle video |
| CN113111518A (en) * | 2021-04-15 | 2021-07-13 | 应急管理部四川消防研究所 | Fire simulation processing method based on Internet of things |
| CN113192282A (en) * | 2021-04-16 | 2021-07-30 | 南京玄甲物联科技有限公司 | Fire early warning system based on internet of things |
| CN113704960A (en) * | 2021-07-12 | 2021-11-26 | 国网河北省电力有限公司电力科学研究院 | Method for determining fire spreading speed of parallel double cables |
| CN113704960B (en) * | 2021-07-12 | 2024-02-20 | 国网河北省电力有限公司电力科学研究院 | Method for determining fire spreading speed of parallel double cables |
| CN117592789A (en) * | 2024-01-18 | 2024-02-23 | 山东金桥保安器材有限公司 | Power grid environment fire risk assessment method and equipment based on time sequence analysis |
| CN117592789B (en) * | 2024-01-18 | 2024-04-16 | 山东金桥保安器材有限公司 | Power grid environment fire risk assessment method and equipment based on time sequence analysis |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109472421A (en) | A kind of power grid mountain fire sprawling method for early warning and device | |
| KR102076426B1 (en) | System for managing detailed weather prediction information in real time and method to use for impact forecasting of heat-wave and tropical night using the system | |
| CN103870891B (en) | Fire disaster of power transmission line based on grid stream spreads Forecasting Methodology and system | |
| US11660480B2 (en) | Fire forecasting | |
| Kafy et al. | Prediction of seasonal urban thermal field variance index using machine learning algorithms in Cumilla, Bangladesh | |
| Domeisen et al. | Prediction and projection of heatwaves | |
| US11202926B2 (en) | Fire monitoring | |
| US11169678B2 (en) | Fire management tool with versatile user interface | |
| Kwon | Uncertainty analysis of wind energy potential assessment | |
| Bonazountas et al. | A decision support system for managing forest fire casualties | |
| Miller et al. | Assessing risk to birds from industrial wind energy development via paired resource selection models | |
| Kochanski et al. | Evaluation of WRF-SFIRE performance with field observations from the FireFlux experiment | |
| Coffield et al. | Machine learning to predict final fire size at the time of ignition | |
| San-Miguel-Ayanz et al. | Current methods to assess fire danger potential | |
| CN103472326A (en) | Method for evaluating probability of forest fire induced transmission line fault | |
| CN109448292A (en) | A kind of power grid mountain fire monitoring and pre-alarming method | |
| CN103905560B (en) | Oil tank area flame spread prediction and analysis method based on complex networks | |
| WO2020106720A1 (en) | Fire monitoring | |
| CN106157178A (en) | A kind of power transmission line corridor periphery mountain fire prediction of the development trend method | |
| Vajda et al. | Assessment of forest fire danger in a boreal forest environment: description and evaluation of the operational system applied in F inland | |
| Chen et al. | Spatial patterns and drivers for wildfire ignitions in California | |
| Dacre et al. | Chilean wildfires: probabilistic prediction, emergency response, and public communication | |
| CN116307739A (en) | Forest fire prevention intelligent monitoring early warning system | |
| KR20230083842A (en) | Wildfire spread monitoring system for facility protection | |
| Wang et al. | Assessing the impact of climate change on flood in an alpine catchment using multiple hydrological models |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20190315 |