CN110728448A - Power grid disaster risk assessment method and device based on strong precipitation space-time distribution characteristics - Google Patents

Power grid disaster risk assessment method and device based on strong precipitation space-time distribution characteristics Download PDF

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CN110728448A
CN110728448A CN201910960260.6A CN201910960260A CN110728448A CN 110728448 A CN110728448 A CN 110728448A CN 201910960260 A CN201910960260 A CN 201910960260A CN 110728448 A CN110728448 A CN 110728448A
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disaster
terrain
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place
standard deviation
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梁允
李哲
王晓芳
刘善峰
王津宇
汪晓康
李帅
王婧羽
崔晶晶
赖安伟
苑司坤
崔春光
高阳
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Wuhan Rainstorm Research Institute Of China Meteorological Administration
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Henan Electric Power Co Ltd
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Wuhan Rainstorm Research Institute Of China Meteorological Administration
State Grid Corp of China SGCC
Electric Power Research Institute of State Grid Henan Electric Power Co Ltd
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Abstract

The application relates to a power grid disaster risk assessment method and device based on strong rainfall space-time distribution characteristics, precipitation data of a specific area, power supply quantity, power consumption of the whole society, general domestic production total value (GDP), high-resolution terrain, water system and other data are adopted, power grid disaster risk zoning is carried out on the basis of comprehensively considering disaster-causing factor (rainstorm and short-time strong rainfall) dangerousness, pregnant disaster environment (terrain and water system) sensibility, disaster-bearing body (power supply quantity and power consumption) vulnerability and disaster prevention and resistance (man-based GDP), and by the zoning method, high-risk areas of a plurality of power grid strong rainfall disasters of the specific area can be identified, and scientific meteorological information can be provided for power grid design, maintenance and decision-making service.

Description

Power grid disaster risk assessment method and device based on strong precipitation space-time distribution characteristics
Technical Field
The application belongs to the technical field of power grid disaster risk division correlation, and particularly relates to a power grid disaster risk assessment method and device based on strong precipitation space-time distribution characteristics.
Background
China is one of countries with large frequency, influence degree and influence range of rainstorm weather in the world, disasters caused by local strong rainfall in China, particularly strong rainfall generated in a short time are very serious, four river areas of a sea river, a yellow river, a Changjiang river and a Huaihe river are spanned in specific areas, namely Taihang mountains, Funiu mountains, Tongbai mountains-Dabie mountains, south-Yang basins and Huang-Huai-Hai plain, weather influence systems are complex, rainfall space-time distribution is not uniform, continuous extra-large rainstorm occurs in specific areas and the like, so that a plurality of reservoirs such as plate bridge reservoirs and the like span dams, and personnel and economic losses are huge. With the development of economy, long-distance power transmission lines in a specific area are increased day by day, the power supply quantity and the power consumption quantity are increased continuously, and the influence of a complex meteorological environment on the power transmission safety is obvious day by day.
The existing power grid disaster risk zoning technology has the following problems: the meteorological elements related to the national standard of the current power transmission line design specification in China mainly include atmospheric temperature, wind speed, icing and thunder, precipitation is not considered, strong precipitation can also cause serious power grid disasters, the disasters mainly relate to three types of rain-flashing accidents, equipment flooding and mechanical faults, if the power grid is designed and transformed in a construction process, a meteorological department provides accurate microclimate data along the power grid, the design standards and the construction requirements of lines and towers can be determined, in addition, most of the past research work only focuses on rainstorm, the influence of short-time strong precipitation on power grid disaster risks is less involved, the research surface is single, and the integrated disaster risk division is not facilitated.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: in order to overcome the defects in the prior art, the power grid disaster risk assessment method and device based on the strong precipitation space-time distribution characteristics are provided, and the disaster risk degree of each place can be divided.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a power grid disaster risk assessment method based on strong precipitation space-time distribution characteristics comprises the following steps:
step 1: calculating the dangerousness of the disaster causing factor based on the rainfall data of each place in the specific area;
step 2: calculating the environmental sensitivity of pregnant disaster based on the terrain elevation data of each place;
and step 3: calculating the vulnerability of the disaster-bearing body based on the power supply amount and the power consumption amount data of each place;
and 4, step 4: calculating disaster prevention and resistance capacity based on the per-capita GDP data of counties and cities of each place;
and 5: and (4) obtaining the power grid strong rainfall disaster risk indexes of different places in each place of the specific area according to the dangerousness of the disaster-causing factors, the pregnant disaster environment sensitivity, the vulnerability of the disaster-bearing body and the disaster prevention and resistance capability in the steps 1-4.
Preferably, the power grid disaster risk assessment method based on the strong precipitation space-time distribution characteristics,
the calculation of the risk of the disaster causing factor comprises the following steps:
step 11: carrying out distribution statistics on rainfall and frequency of rainstorms which last for 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days or more in each place of the specific area every year and are rainstorms for at least one day and/or short-time heavy rainfall for at least one hour, and calculating the accumulated rainfall during rainfall according to the rainfall;
step 12: classifying grades according to the accumulated rainfall and endowing each grade with a coefficient;
step 13: multiplying the frequency of rainstorm and/or short-time heavy rainfall corresponding to each grade by the grade coefficient, then adding all the grades to obtain rainstorm risk indexes and/or short-time heavy rainfall risk indexes of different places in a specific area, and carrying out breakpoint according to the rainstorm risk indexes of all the places in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes to endow all the places in the specific area with risk values A of disaster-causing factors;
and/or
The method for calculating the environmental sensitivity of pregnancy disasters comprises the following steps:
step 31: obtaining the terrain elevation of a certain place according to the terrain height data, and calculating the standard deviation of eight adjacent points around the point to obtain the terrain standard deviation;
step 32: assigning a value to the terrain influence coefficient according to the combination condition of the terrain elevation and the terrain standard deviation;
step 33: assigning the influence coefficient of the water system according to the conditions of rivers, lakes and reservoirs in the site;
step 34: obtaining a comprehensive value of the pregnant disaster environment sensitivity according to the terrain influence coefficient and the water system influence coefficient;
step 35: acquiring the distribution condition of the comprehensive disaster-prone environment sensitivity values of each place in the specific area, and giving a comprehensive disaster-prone environment sensitivity value B to each place according to the position of the comprehensive disaster-prone environment sensitivity values of each place in the distribution condition;
and/or
The calculation of vulnerability of the disaster-bearing body comprises the following steps:
counting the power supply quantity and the total power consumption quantity data of counties and cities of each place in the specific area, acquiring the distribution condition of the power supply quantity and the total power consumption quantity data of the counties and cities of each place in the specific area, and giving a vulnerability numerical value C of a disaster-bearing body of each place according to the position of each place in the distribution condition of the counties and cities;
and/or
The calculation of the disaster prevention and resistance capability comprises the following steps:
and counting the GDP data of the prefectures in the counties and cities of each place in the specific area, acquiring the distribution condition of the GDP data of the prefectures in the counties and cities of each place in the specific area, and giving disaster prevention and resistance capacity values D to each place according to the positions of the distribution condition of the prefectures in the counties and cities.
Preferably, the power grid disaster risk assessment method based on the strong precipitation space-time distribution characteristics,
in the calculation of the risk of the disaster-causing factor, the maximum values of the accumulated rainfall of the heavy rain and the accumulated rainfall of the short-time heavy rainfall are divided into 1-5 grades according to five numerical value ranges of 60% -80%, 80% -90%, 90% -95%, 95% -98% and more than 98%, and coefficients of 1/15, 2/15, 3/15, 4/15 and 5/15 are respectively given;
the percentage values of breakpoints of the rainstorm risk indexes of all the sites in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes are respectively 20%, 40%, 60% and 80%, and the risk values A of the disaster-causing factors of the sites are respectively defined as 1, 2, 3, 4 and 5;
in the calculation of the pregnant disaster environment sensitivity, the combined situation of the terrain elevation and the terrain standard deviation is used for assigning values to the terrain influence coefficients, and the values are respectively as follows:
when the terrain elevation H is less than 100m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m which is less than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d which is more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 100m and less than 300m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 300m and less than 700m, the assignment of a terrain standard deviation grade d <1m is 0.8, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.7, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.6;
when the terrain elevation H is larger than or equal to 700m, the assignment of the terrain standard deviation grade d <1m is 0.7, the assignment of the terrain standard deviation grade d <10m which is smaller than or equal to 1m is 0.6, and the assignment of the terrain standard deviation grade d larger than or equal to 10m is 0.5;
the influence coefficients of the water system are assigned to be within 8km from a first-level river and within 6km from a second-level river according to the conditions of the rivers, the lakes and the reservoirs in the sites, the influence coefficients of the water system are 0.8, the influence coefficients of the water system are 0.4 and the coefficients between the two distances are 0.4-0.8, and the influence coefficients of the water system are beyond 12km from the first-level river and 10km from the second-level river;
adding the two assignments, then averaging, carrying out numerical breakpoint according to the proportion of 20%, 40%, 60% and 80%, and respectively defining the pregnant disaster environment sensitivity values B of the sites as 1, 2, 3, 4 and 5;
in the calculation of the vulnerability of the disaster-bearing body, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of the total data of the power supply quantity and the power consumption quantity in a specific area, and the vulnerability numerical values C of the disaster-bearing body of the site are respectively defined to be 1, 2, 3, 4 and 5;
in the calculation of the disaster prevention and resistance capability, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of GDP data of people in counties and cities of each place in a specific area, and the disaster prevention and resistance capability numerical values D of the places are respectively defined as 1, 2, 3, 4 and 5.
Preferably, the power grid disaster risk assessment method based on the strong precipitation space-time distribution characteristics,
power grid heavy rainfall disaster risk index DR ═ A0.3×B0.3×C0.2×D0.2。
Preferably, according to the power grid disaster risk assessment method based on the strong rainfall spatial-temporal distribution characteristics, the power grid strong rainfall disaster risk indexes of the site are respectively corresponding to low risk, medium-high risk and high risk when the power grid strong rainfall disaster risk indexes are less than 20%, 20% -40%, 40% -60%, 60% -80% and more than 80% of the total distribution of the power grid strong rainfall disaster risk indexes.
The invention also provides a power grid disaster risk assessment device based on the strong precipitation space-time distribution characteristics, which comprises,
a risk calculation module of the disaster causing factor: calculating the dangerousness of the disaster causing factor based on the rainfall data of each place in the specific area;
a pregnant disaster environment sensitivity calculation module: calculating the environmental sensitivity of pregnant disaster based on the terrain elevation data of each place;
disaster-bearing body vulnerability calculation module: calculating the vulnerability of the disaster-bearing body based on the power supply amount and the power consumption amount data of each place;
the disaster prevention and resistance calculation module: calculating disaster prevention and resistance capacity based on the per-capita GDP data of counties and cities of each place;
the power grid heavy precipitation disaster risk index calculation module: and obtaining the power grid heavy rainfall disaster risk indexes of different places in each place of the specific area according to the danger of the disaster causing factor, the pregnant disaster environment sensitivity, the vulnerability of a disaster bearing body and the disaster prevention and resistance capability.
Preferably, the power grid disaster risk assessment device based on the strong precipitation space-time distribution characteristics of the invention,
the calculation of the risk of the disaster causing factor comprises the following steps:
step 11: carrying out distribution statistics on rainfall and frequency of rainstorms which last for 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days or more in each place of the specific area every year and are rainstorms for at least one day and/or short-time heavy rainfall for at least one hour, and calculating the accumulated rainfall during rainfall according to the rainfall;
step 12: classifying grades according to the accumulated rainfall and endowing each grade with a coefficient;
step 13: multiplying the frequency of rainstorm and/or short-time heavy rainfall corresponding to each grade by the grade coefficient, then adding all the grades to obtain rainstorm risk indexes and/or short-time heavy rainfall risk indexes of different places in a specific area, and carrying out breakpoint according to the rainstorm risk indexes of all the places in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes to endow all the places in the specific area with risk values A of disaster-causing factors;
and/or
The method for calculating the environmental sensitivity of pregnancy disasters comprises the following steps:
step 31: obtaining the terrain elevation of a certain place according to the terrain height data, and calculating the standard deviation of eight adjacent points around the point to obtain the terrain standard deviation;
step 32: assigning a value to the terrain influence coefficient according to the combination condition of the terrain elevation and the terrain standard deviation;
step 33: assigning the influence coefficient of the water system according to the conditions of rivers, lakes and reservoirs in the site;
step 34: obtaining a comprehensive value of the pregnant disaster environment sensitivity according to the terrain influence coefficient and the water system influence coefficient;
step 35: acquiring the distribution condition of the comprehensive disaster-prone environment sensitivity values of each place in the specific area, and giving a comprehensive disaster-prone environment sensitivity value B to each place according to the position of the comprehensive disaster-prone environment sensitivity values of each place in the distribution condition;
and/or
The calculation of vulnerability of the disaster-bearing body comprises the following steps:
counting the power supply quantity and the total power consumption quantity data of counties and cities of each place in the specific area, acquiring the distribution condition of the power supply quantity and the total power consumption quantity data of the counties and cities of each place in the specific area, and giving a vulnerability numerical value C of a disaster-bearing body of each place according to the position of each place in the distribution condition of the counties and cities;
and/or
The calculation of the disaster prevention and resistance capability comprises the following steps:
and counting the GDP data of the prefectures in the counties and cities of each place in the specific area, acquiring the distribution condition of the GDP data of the prefectures in the counties and cities of each place in the specific area, and giving disaster prevention and resistance capacity values D to each place according to the positions of the distribution condition of the prefectures in the counties and cities.
Preferably, the power grid disaster risk assessment device based on the strong precipitation space-time distribution characteristics of the invention,
in the calculation of the risk of the disaster-causing factor, the maximum values of the accumulated rainfall of the heavy rain and the accumulated rainfall of the short-time heavy rainfall are divided into 1-5 grades according to five numerical value ranges of 60% -80%, 80% -90%, 90% -95%, 95% -98% and more than 98%, and coefficients of 1/15, 2/15, 3/15, 4/15 and 5/15 are respectively given;
the percentage values of breakpoints of the rainstorm risk indexes of all the sites in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes are respectively 20%, 40%, 60% and 80%, and the risk values A of the disaster-causing factors of the sites are respectively defined as 1, 2, 3, 4 and 5;
in the calculation of the pregnant disaster environment sensitivity, the combined situation of the terrain elevation and the terrain standard deviation is used for assigning values to the terrain influence coefficients, and the values are respectively as follows:
when the terrain elevation H is less than 100m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m which is less than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d which is more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 100m and less than 300m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 300m and less than 700m, the assignment of a terrain standard deviation grade d <1m is 0.8, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.7, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.6;
when the terrain elevation H is larger than or equal to 700m, the assignment of the terrain standard deviation grade d <1m is 0.7, the assignment of the terrain standard deviation grade d <10m which is smaller than or equal to 1m is 0.6, and the assignment of the terrain standard deviation grade d larger than or equal to 10m is 0.5;
the influence coefficients of the water system are assigned to be within 8km from a first-level river and within 6km from a second-level river according to the conditions of the rivers, the lakes and the reservoirs in the sites, the influence coefficients of the water system are 0.8, the influence coefficients of the water system are 0.4 and the coefficients between the two distances are 0.4-0.8, and the influence coefficients of the water system are beyond 12km from the first-level river and 10km from the second-level river;
adding the two assignments, then averaging, carrying out numerical breakpoint according to the proportion of 20%, 40%, 60% and 80%, and respectively defining the pregnant disaster environment sensitivity values B of the sites as 1, 2, 3, 4 and 5;
in the calculation of the vulnerability of the disaster-bearing body, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of the total data of the power supply quantity and the power consumption quantity in a specific area, and the vulnerability numerical values C of the disaster-bearing body of the site are respectively defined to be 1, 2, 3, 4 and 5;
in the calculation of the disaster prevention and resistance capability, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of GDP data of people in counties and cities of each place in a specific area, and the disaster prevention and resistance capability numerical values D of the places are respectively defined as 1, 2, 3, 4 and 5.
Preferably, the power grid disaster risk assessment device based on the strong precipitation space-time distribution characteristics of the invention,
power grid heavy rainfall disaster risk index DR ═ A0.3×B0.3×C0.2×D0.2。
Preferably, according to the power grid disaster risk assessment method based on the strong rainfall spatial-temporal distribution characteristics, the power grid strong rainfall disaster risk indexes of the site are respectively corresponding to low risk, medium-high risk and high risk when the power grid strong rainfall disaster risk indexes are less than 20%, 20% -40%, 40% -60%, 60% -80% and more than 80% of the total distribution of the power grid strong rainfall disaster risk indexes.
The invention has the beneficial effects that:
the invention relates to a power grid disaster risk assessment method and device based on strong rainfall space-time distribution characteristics, which adopt near-year-day rainfall data of a specific area, power supply quantity, power consumption of the whole society, a man-to-home production total value (GDP), high-resolution terrain, a water system and other data, carry out power grid disaster risk zoning on the basis of comprehensively considering disaster-causing factor (rainstorm and short-time strong rainfall) dangerousness, pregnant disaster environment (terrain and water system) sensibility, disaster-bearing body (power supply quantity and power consumption) vulnerability and disaster prevention and resistance (man-to-home GDP).
Drawings
The technical solution of the present application is further explained below with reference to the drawings and the embodiments.
FIGS. 1a and 1b are schematic diagrams of the annual average rainstorm frequency distribution and the annual cumulative short-term heavy precipitation frequency distribution in a specific region over years according to the present invention;
fig. 2a, 2b, and 2c are schematic diagrams of disaster-causing factor risk level divisions of a rainstorm, short-time heavy precipitation, and grid heavy precipitation disaster in a specific area according to the present invention, respectively;
3a, 3b and 3c are schematic diagrams of the power grid heavy rainfall terrain and water system influence coefficient distribution in a specific area and the power grid heavy rainfall disaster pregnancy disaster environment sensitivity level division provided by the invention;
fig. 4 is a schematic diagram of the final power grid heavy precipitation disaster risk level division according to the embodiment of the present invention.
Detailed Description
It should be noted that the embodiments and features of the embodiments in the present application may be combined with each other without conflict.
The technical solutions of the present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Example 1
The embodiment provides a power grid disaster risk assessment method based on strong precipitation space-time distribution characteristics, which comprises the following steps of:
step 1: calculating the dangerousness of the disaster causing factor based on the rainfall data of each place in the specific area;
step 2: calculating the environmental sensitivity of pregnant disaster based on the terrain elevation data of each place;
and step 3: calculating the vulnerability of the disaster-bearing body based on the power supply amount and the power consumption amount data of each place;
and 4, step 4: calculating disaster prevention and resistance capacity based on the per-capita GDP data of counties and cities of each place;
and 5: and (4) obtaining the power grid strong rainfall disaster risk indexes of different places in each place of the specific area according to the dangerousness of the disaster-causing factors, the pregnant disaster environment sensitivity, the vulnerability of the disaster-bearing body and the disaster prevention and resistance capability in the steps 1-4.
The calculation of the risk of the disaster causing factor comprises the following steps:
step 11: carrying out distribution statistics on rainfall and frequency of rainstorms which last for 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days or more in each place of the specific area every year and are rainstorms for at least one day and/or short-time heavy rainfall for at least one hour, and calculating the accumulated rainfall during rainfall according to the rainfall;
step 12: classifying grades according to the accumulated rainfall and endowing each grade with a coefficient;
step 13: multiplying the frequency of rainstorm and/or short-time heavy rainfall corresponding to each grade by the grade coefficient, then adding all the grades to obtain rainstorm risk indexes and/or short-time heavy rainfall risk indexes of different places in a specific area, and carrying out breakpoint according to the rainstorm risk indexes of all the places in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes to endow all the places in the specific area with risk values A of disaster-causing factors;
the method for calculating the environmental sensitivity of pregnancy disasters comprises the following steps:
step 31: obtaining the terrain elevation of a certain place according to the terrain height data, and calculating the standard deviation of eight adjacent points around the point to obtain the terrain standard deviation;
step 32: assigning a value to the terrain influence coefficient according to the combination condition of the terrain elevation and the terrain standard deviation;
step 33: assigning the influence coefficient of the water system according to the conditions of rivers, lakes and reservoirs in the site;
step 34: obtaining a comprehensive value of the pregnant disaster environment sensitivity according to the terrain influence coefficient and the water system influence coefficient;
step 35: acquiring the distribution condition of the comprehensive disaster-prone environment sensitivity values of each place in the specific area, and giving a comprehensive disaster-prone environment sensitivity value B to each place according to the position of the comprehensive disaster-prone environment sensitivity values of each place in the distribution condition;
the calculation of vulnerability of the disaster-bearing body comprises the following steps:
counting the power supply quantity and the total power consumption quantity data of counties and cities of each place in the specific area, acquiring the distribution condition of the power supply quantity and the total power consumption quantity data of the counties and cities of each place in the specific area, and giving a vulnerability numerical value C of a disaster-bearing body of each place according to the position of each place in the distribution condition of the counties and cities;
the calculation of the disaster prevention and resistance capability comprises the following steps:
and counting the GDP data of the prefectures in the counties and cities of each place in the specific area, acquiring the distribution condition of the GDP data of the prefectures in the counties and cities of each place in the specific area, and giving disaster prevention and resistance capacity values D to each place according to the positions of the distribution condition of the prefectures in the counties and cities.
In the calculation of the risk of the disaster-causing factor, the maximum values of the accumulated rainfall of the heavy rain and the accumulated rainfall of the short-time heavy rainfall are divided into 1-5 grades according to five numerical value ranges of 60% -80%, 80% -90%, 90% -95%, 95% -98% and more than 98%, and coefficients of 1/15, 2/15, 3/15, 4/15 and 5/15 are respectively given;
the percentage values of breakpoints of the rainstorm risk indexes of all the sites in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes are respectively 20%, 40%, 60% and 80%, and the risk values A of the disaster-causing factors of the sites are respectively defined as 1, 2, 3, 4 and 5;
in the calculation of the pregnant disaster environment sensitivity, the combined situation of the terrain elevation and the terrain standard deviation is used for assigning values to the terrain influence coefficients, and the values are respectively as follows:
when the terrain elevation H is less than 100m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m which is less than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d which is more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 100m and less than 300m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 300m and less than 700m, the assignment of a terrain standard deviation grade d <1m is 0.8, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.7, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.6;
when the terrain elevation H is larger than or equal to 700m, the assignment of the terrain standard deviation grade d <1m is 0.7, the assignment of the terrain standard deviation grade d <10m which is smaller than or equal to 1m is 0.6, and the assignment of the terrain standard deviation grade d larger than or equal to 10m is 0.5;
the influence coefficients of the water system are assigned to be within 8km from a first-level river and within 6km from a second-level river according to the conditions of the rivers, the lakes and the reservoirs in the sites, the influence coefficients of the water system are 0.8, the influence coefficients of the water system are 0.4 and the coefficients between the two distances are 0.4-0.8, and the influence coefficients of the water system are beyond 12km from the first-level river and 10km from the second-level river;
adding the two assignments, then averaging, carrying out numerical breakpoint according to the proportion of 20%, 40%, 60% and 80%, and respectively defining the pregnant disaster environment sensitivity values B of the sites as 1, 2, 3, 4 and 5;
in the calculation of the vulnerability of the disaster-bearing body, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of the total data of the power supply quantity and the power consumption quantity in a specific area, and the vulnerability numerical values C of the disaster-bearing body of the site are respectively defined to be 1, 2, 3, 4 and 5;
in the calculation of the disaster prevention and resistance capability, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of GDP data of people in counties and cities of each place in a specific area, and the disaster prevention and resistance capability numerical values D of the places are respectively defined as 1, 2, 3, 4 and 5.
Power grid heavy rainfall disaster risk index DR ═ A0.3×B0.3×C0.2×D0.2。
And when the power grid heavy rainfall disaster risk indexes of the site are less than 20%, 20-40%, 40-60%, 60-80% and more than 80% of the total distribution of the power grid heavy rainfall disaster risk indexes, the power grid heavy rainfall disaster risk indexes correspond to low risk, medium and high risk respectively.
Example 2
The embodiment provides a power grid disaster risk assessment device based on strong precipitation space-time distribution characteristics, which comprises,
a risk calculation module of the disaster causing factor: calculating the dangerousness of the disaster causing factor based on the rainfall data of each place in the specific area;
a pregnant disaster environment sensitivity calculation module: calculating the environmental sensitivity of pregnant disaster based on the terrain elevation data of each place;
disaster-bearing body vulnerability calculation module: calculating the vulnerability of the disaster-bearing body based on the power supply amount and the power consumption amount data of each place;
the disaster prevention and resistance calculation module: calculating disaster prevention and resistance capacity based on the per-capita GDP data of counties and cities of each place;
the power grid heavy precipitation disaster risk index calculation module: and obtaining the power grid heavy rainfall disaster risk indexes of different places in each place of the specific area according to the danger of the disaster causing factor, the pregnant disaster environment sensitivity, the vulnerability of a disaster bearing body and the disaster prevention and resistance capability.
The calculation of the risk of the disaster causing factor comprises the following steps:
step 11: carrying out distribution statistics on rainfall and frequency of rainstorms which last for 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days or more in each place of the specific area every year and are rainstorms for at least one day and/or short-time heavy rainfall for at least one hour, and calculating the accumulated rainfall during rainfall according to the rainfall;
step 12: classifying grades according to the accumulated rainfall and endowing each grade with a coefficient;
step 13: multiplying the frequency of rainstorm and/or short-time heavy rainfall corresponding to each grade by the grade coefficient, then adding all the grades to obtain rainstorm risk indexes and/or short-time heavy rainfall risk indexes of different places in a specific area, and carrying out breakpoint according to the rainstorm risk indexes of all the places in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes to endow all the places in the specific area with risk values A of disaster-causing factors;
and/or
The method for calculating the environmental sensitivity of pregnancy disasters comprises the following steps:
step 31: obtaining the terrain elevation of a certain place according to the terrain height data, and calculating the standard deviation of eight adjacent points around the point to obtain the terrain standard deviation;
step 32: assigning a value to the terrain influence coefficient according to the combination condition of the terrain elevation and the terrain standard deviation;
step 33: assigning the influence coefficient of the water system according to the conditions of rivers, lakes and reservoirs in the site;
step 34: obtaining a comprehensive value of the pregnant disaster environment sensitivity according to the terrain influence coefficient and the water system influence coefficient;
step 35: acquiring the distribution condition of the comprehensive disaster-prone environment sensitivity values of each place in the specific area, and giving a comprehensive disaster-prone environment sensitivity value B to each place according to the position of the comprehensive disaster-prone environment sensitivity values of each place in the distribution condition;
and/or
The calculation of vulnerability of the disaster-bearing body comprises the following steps:
counting the power supply quantity and the total power consumption quantity data of counties and cities of each place in the specific area, acquiring the distribution condition of the power supply quantity and the total power consumption quantity data of the counties and cities of each place in the specific area, and giving a vulnerability numerical value C of a disaster-bearing body of each place according to the position of each place in the distribution condition of the counties and cities;
and/or
The calculation of the disaster prevention and resistance capability comprises the following steps:
and counting the GDP data of the prefectures in the counties and cities of each place in the specific area, acquiring the distribution condition of the GDP data of the prefectures in the counties and cities of each place in the specific area, and giving disaster prevention and resistance capacity values D to each place according to the positions of the distribution condition of the prefectures in the counties and cities.
In the calculation of the risk of the disaster-causing factor, the maximum values of the accumulated rainfall of the heavy rain and the accumulated rainfall of the short-time heavy rainfall are divided into 1-5 grades according to five numerical value ranges of 60% -80%, 80% -90%, 90% -95%, 95% -98% and more than 98%, and coefficients of 1/15, 2/15, 3/15, 4/15 and 5/15 are respectively given;
the percentage values of breakpoints of the rainstorm risk indexes of all the sites in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes are respectively 20%, 40%, 60% and 80%, and the risk values A of the disaster-causing factors of the sites are respectively defined as 1, 2, 3, 4 and 5;
in the calculation of the pregnant disaster environment sensitivity, the combined situation of the terrain elevation and the terrain standard deviation is used for assigning values to the terrain influence coefficients, and the values are respectively as follows:
when the terrain elevation H is less than 100m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m which is less than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d which is more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 100m and less than 300m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 300m and less than 700m, the assignment of a terrain standard deviation grade d <1m is 0.8, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.7, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.6;
when the terrain elevation H is larger than or equal to 700m, the assignment of the terrain standard deviation grade d <1m is 0.7, the assignment of the terrain standard deviation grade d <10m which is smaller than or equal to 1m is 0.6, and the assignment of the terrain standard deviation grade d larger than or equal to 10m is 0.5;
the influence coefficients of the water system are assigned to be within 8km from a first-level river and within 6km from a second-level river according to the conditions of the rivers, the lakes and the reservoirs in the sites, the influence coefficients of the water system are 0.8, the influence coefficients of the water system are 0.4 and the coefficients between the two distances are 0.4-0.8, and the influence coefficients of the water system are beyond 12km from the first-level river and 10km from the second-level river;
adding the two assignments, then averaging, carrying out numerical breakpoint according to the proportion of 20%, 40%, 60% and 80%, and respectively defining the pregnant disaster environment sensitivity values B of the sites as 1, 2, 3, 4 and 5;
in the calculation of the vulnerability of the disaster-bearing body, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of the total data of the power supply quantity and the power consumption quantity in a specific area, and the vulnerability numerical values C of the disaster-bearing body of the site are respectively defined to be 1, 2, 3, 4 and 5;
in the calculation of the disaster prevention and resistance capability, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of GDP data of people in counties and cities of each place in a specific area, and the disaster prevention and resistance capability numerical values D of the places are respectively defined as 1, 2, 3, 4 and 5.
Power grid heavy rainfall disaster risk index DR ═ A0.3×B0.3×C0.2×D0.2。
And when the power grid heavy rainfall disaster risk indexes of the site are less than 20%, 20-40%, 40-60%, 60-80% and more than 80% of the total distribution of the power grid heavy rainfall disaster risk indexes, the power grid heavy rainfall disaster risk indexes correspond to low risk, medium and high risk respectively.
The embodiment provides a power grid disaster risk assessment method based on strong precipitation space-time distribution characteristics, which comprises the following steps of:
step 1: calculating the strong rainfall space-time distribution condition by using rainfall data of a specific area (the rainfall data is generally day rainfall and 6-hour rainfall data in recent years, mainly the number of rainstorm days and the number of times of short-time strong rainfall), and analyzing the change trend of the strong rainfall intensity;
step 2: calculating the risk of the disaster-causing factor based on the change trend of the strong rainfall intensity;
and step 3: calculating the environmental sensitivity of pregnancy disaster based on the terrain elevation data;
and 4, step 4: calculating the vulnerability of the disaster-bearing body based on the power supply quantity and the power consumption quantity data;
and 5: based on GDP data of people in each county and city in a specific area, calculating the ratio of the disaster resistance to the precipitation magnitude of each county and city, and calculating the disaster prevention and resistance;
step 6: and (5) obtaining a power grid strong rainfall disaster risk index according to the dangerousness of the disaster causing factors, the pregnant disaster environment sensitivity, the vulnerability of disaster-bearing bodies and the disaster prevention and resistance capability in the steps 2 to 5.
Effects of the embodiment
In the embodiment, Henan province is selected as a research object, and the specific working principle and the use flow of the method are as follows:
(1) calculation of risk of disaster-causing factor-consideration of strong precipitation spatial distribution
Two types of precipitation which are easy to cause flood disasters are mainly considered, one type is rainstorm, and the other type is short-time strong precipitation. By utilizing the years of daily rainfall data of 119 national stations in a specific area and the years of hourly rainfall data provided by a national weather information center, rainstorm is defined as rainfall with the daily rainfall reaching 50mm, and short-term strong rainfall is defined as rainfall with the hourly rainfall reaching 20mm, and the annual average rainstorm and the short-term strong rainfall frequency of each station are calculated.
Data of precipitation in the last 53 years and precipitation in the last 6 hours in each region of Henan province are obtained and analyzed, and the obtained results are shown in attached figures 1a and 1 b.
From fig. 1a and 1b, it can be seen that the rainstorm frequency of a specific area gradually decreases from south to north and from east to west, the annual average rainstorm frequency is maximum, and above 4 times, the maximum is a Xinyang chicken mountain station, and the annual average is 5.2 times; the frequency of rainstorm of the plain of Huang-Huai-Hai and the south-yang basin is more than 2-4 times; the rainstorm frequency of Zhengzhou, Anyang and Puyang in the mountain area of Yuxi, Taihang mountain and the district of the middle north is the lowest and is between 0.7 and 2 times. As can be seen from fig. 1b, the distribution of the number of short-term strong precipitation times generally decreases from southeast to northwest, but the difference between the stations is large. The northern place of the Funiu mountain in the mountainous region of Hexi is a low-value region of the short-time strong precipitation times, wherein the site with the least times is located in Luonin county, and the number of the sites is only 2. The stations with more times are concentrated in the places of the Chinese arborvitae and Dabie mountain, the places in the yellow river and the north of the yellow river of the Huang-Huai-Hai plain, the places of the Funishan mountain and the middle and west part of the Shang, the number of the stations with more than 20 times in four areas respectively reaches 8, 5, 2 and 2, and the station with the most times is located in the fixed county and reaches 31 times. The number of short-time strong precipitation times of other places is more than 6-15.
The division of the rainstorm and short-term heavy rainfall levels is obtained by years of live data statistics, and the following table shows the rainstorm accumulated rainfall ranges of different levels for different duration days. The rainstorm in a specific area for many years is 12710 times, processes lasting for 1, 2, 3, 4, 5, 6d and 7d and above are counted respectively (the station has rainfall every day, and at least one d reaches the rainstorm magnitude), the accumulated rainfall in the rainstorm process is calculated considering that the greater the accumulated rainfall is, the greater the possibility of causing flooding is, the more the accumulated rainfall is, the 1-5 grades are divided into intervals of 60-80%, 80-90%, 90-95%, 95-98% and above 98%, and coefficients of the rainfall are given to 1/15, 2/15, 3/15, 4/15 and 5/15 respectively.
TABLE 1 different duration days, different grades of rainstorm cumulative rainfall (R, unit: mm) ranges
Figure BDA0002228679990000091
Note: days 7 indicate the course of 7d and above
The calculation method of the short-time strong rainfall accumulated rainfall range of different levels is similar to that of rainstorm. 2010, when the short-time strong precipitation occurs in a specific region of a year, 1290 standing times are counted, processes lasting for 1, 2, 3, 4, 5, 6 and 7hr and more (precipitation exists in each hour of the site, at least one hr reaches the short-time strong precipitation magnitude), accumulated rainfall in the processes is calculated, precipitation in the ranges of 60% -80%, 80% -90%, 90% -95%, 95% -98% and more than 98% is divided into 1-5 levels, and coefficients of 1/15, 2/15, 3/15, 4/15 and 5/15 are given respectively.
Calculating the frequency of rainstorm (short-time strong precipitation) of different levels of each rainfall station, multiplying the frequency by the equal level coefficient, then adding all the levels to obtain the rainstorm (short-time strong precipitation) risk of the station, interpolating the station data onto the grid points, and obtaining the rainstorm (short-time strong precipitation) risk level according to 20%, 40%, 60% and 80% percentile numerical breakpoints. And averaging the rainstorm and the short-time strong rainfall to obtain the disaster-causing factor danger level division. Wherein, less than 20%, 20% to 40%, 40% to 60%, 60% to 80%, and more than 80% correspond to low risk, medium risk, and high risk, respectively.
Fig. 2a and 2b show the regions of a particular area with rainstorm, short-term heavy precipitation and disaster-causing factor risk level. Comparing fig. 2a and 2b, it can be seen that the risks of rainstorm and short-term strong precipitation decrease from south to north and from east to west, and the differences are mainly reflected in the south-yang location, the horseback shop location and the Dabie mountain location. Specifically, the heavy rain high-risk area is located in the south-yang basin, the short-time heavy rain high-risk area is located in the middle and west of the south-yang basin, and the basin is a low-risk area; the locations of the horse-building shops and the Dabie mountain are areas with high rainstorm risk, but the short-time strong rainfall risk is of a medium-low level; the short-term strong precipitation is high in the risk of the Wang mountain and the Puyang place, and the heavy rain is low and medium-low.
Fig. 2c is a region diagram of the risk level of the disaster-causing factor, in which it can be seen that the disaster-causing factor combining the rainstorm and the short-term heavy rainfall shows that the place with higher risk is the east of Funishan mountain to Tumbeshan mountain, the north of Dabieshan mountain, the east of Loxowo city, Zhou kou city, Shang city and the east of Xinxiang and Anyang, and the risk in West Henan mountain is relatively minimum.
(2) Computing environmental susceptibility to pregnancy hazards
According to the method, the terrain elevation of a certain point is obtained by utilizing SRTM terrain height data with 90m resolution, and the standard deviation of eight adjacent points around the point is calculated to obtain the terrain standard deviation. And table 2 shows different combination assignments of terrain elevation and terrain standard deviation, and the lower the terrain height of a certain point and the lower the standard deviation of the surrounding terrain, the more easily waterlogging is formed at the point.
TABLE 2 Combined assignment of terrain elevation (H) and terrain standard deviation (d) (terrain influence coefficient)
Figure BDA0002228679990000101
FIG. 3 shows the landform of strong rainfall of the power grid in a specific area, the distribution of influence coefficients of water systems and the pregnancy of the power grid strong rainfall disasterAn environmental sensitivity level compartment. As shown in fig. 3a, in the eastern Henan plain site, the terrain influence coefficient is above 0.8, and the flood risk degree is high. The terrains influence coefficient is below 0.5 in the places of Funiu mountain in the west of Henan, Dabie mountain in the south, Tuebieshan mountain and Taihang mountain in the north of Henan, and the flood risk degree is lower. Water systems in a specific area are mainly divided into rivers and lake reservoirs, and when calculating the buffer area of the lake reservoir, only the area of 1000 km is generally considered2In the above water areas, the specific area does not reach the area standard, and the influence of the lake and the reservoir is not considered separately when the pregnant disaster environment is calculated. FIG. 3b shows that the first-level river flowing through a specific area has a yellow river, the second-level river has a sea river, a Huaihe river and a Hanjiang river, the distance between the first-level river and the second-level river is within 8km and within 6km of the first-level river, the influence coefficient of the water system is 0.8, the distance between the first-level river and the second-level river is beyond 12km and 10km, the influence coefficient of the water system is 0.4, and the coefficient between the two distances is 0.4-0.8, which is inversely proportional.
The terrain and the water system in the pregnant disaster environment are respectively endowed with 0.5 weight coefficient, the pregnant disaster environment sensitivity is obtained after addition, and the sensitivity is divided into 5 grades according to 20%, 40%, 60% and 80% percentile numerical breakpoints. FIG. 3c is a pregnant disaster environment sensitivity level plot in which the terrain and water system distribution are considered comprehensively, and the sea river, the Huai river, the Han river basin and the yellow river basin at the plain site are areas with the highest disaster risk; most areas of Huang-Huai-Hai plain, Dabie mountain and south-Yang basin are middle-high and middle-low dangerous areas; due to the fact that the terrain elevation is large in other mountainous and hilly lands, precipitation can be discharged quickly, serious flood disasters are not prone to forming, and the sensitivity level of the pregnant disaster environment is low.
(3) Calculating vulnerability of the disaster-bearing body based on the power supply amount and the power consumption amount data of each place;
the disaster prevention and resistance capability is based on the per-capita GDP data of the counties and cities of each place.
(4) Power grid heavy precipitation disaster risk assessment and division
The formula for calculating the risk index of the strong precipitation disaster of the power grid in the specific area is as follows:
power grid heavy rainfall disaster risk index DR ═ A0.3×B0.3×C0.2×D0.2。
Wherein DR represents a disaster risk index, A represents the risk of disaster-causing factors, including rainstorm and short-time strong rainfall, B represents the environment sensitivity of pregnant disasters, including terrains and rivers, C represents the vulnerability of disaster-bearing bodies, including power supply quantity and power consumption quantity, and D represents the disaster prevention and resistance capacity, which is determined by the percentile of GDP (global data processing).
Fig. 4 is a power grid heavy rainfall disaster risk grade division of a specific area, which is obtained by calculating disaster risk indexes in the specific area according to 20%, 40%, 60% and 80% percentile numerical breakpoints.
As can be seen from the figure, the four places with the highest disaster risk level mainly comprise ① Funishan places at the boundary of Nanyang, Flat-topped mountain and Luyang, ② Huanghe Haihe plain river basin at the most part of Xinxiang, the south and east of the crane wall, the east of Anyang and the north of Zhengzhou, ③ Shanzhu city prefecture, ④ Huaihe plain basin from the north of Xinyang to the south of Juma shop, three gorges, economic sources, sunlight, unsealing, Chang, Wenchao, and the places at the periphery have relatively low disaster risk levels and medium risks at other places.
In light of the foregoing description of the preferred embodiments according to the present application, it is to be understood that various changes and modifications may be made without departing from the spirit and scope of the invention. The technical scope of the present application is not limited to the contents of the specification, and must be determined according to the scope of the claims.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

Claims (10)

1. A power grid disaster risk assessment method based on strong precipitation space-time distribution characteristics is characterized by comprising the following steps:
step 1: calculating the dangerousness of the disaster causing factor based on the rainfall data of each place in the specific area;
step 2: calculating the environmental sensitivity of pregnant disaster based on the terrain elevation data of each place;
and step 3: calculating the vulnerability of the disaster-bearing body based on the power supply amount and the power consumption amount data of each place;
and 4, step 4: calculating disaster prevention and resistance capacity based on the per-capita GDP data of counties and cities of each place;
and 5: and (4) obtaining the power grid strong rainfall disaster risk indexes of different places in each place of the specific area according to the dangerousness of the disaster-causing factors, the pregnant disaster environment sensitivity, the vulnerability of the disaster-bearing body and the disaster prevention and resistance capability in the steps 1-4.
2. The method for evaluating the risk of power grid disaster based on the strong precipitation space-time distribution characteristics as claimed in claim 1,
the calculation of the risk of the disaster causing factor comprises the following steps:
step 11: carrying out distribution statistics on rainfall and frequency of rainstorms which last for 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days or more in each place of the specific area every year and are rainstorms for at least one day and/or short-time heavy rainfall for at least one hour, and calculating the accumulated rainfall during rainfall according to the rainfall;
step 12: classifying grades according to the accumulated rainfall and endowing each grade with a coefficient;
step 13: multiplying the frequency of rainstorm and/or short-time heavy rainfall corresponding to each grade by the grade coefficient, then adding all the grades to obtain rainstorm risk indexes and/or short-time heavy rainfall risk indexes of different places in a specific area, and carrying out breakpoint according to the rainstorm risk indexes of all the places in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes to endow all the places in the specific area with risk values A of disaster-causing factors;
and/or
The method for calculating the environmental sensitivity of pregnancy disasters comprises the following steps:
step 31: obtaining the terrain elevation of a certain place according to the terrain height data, and calculating the standard deviation of eight adjacent points around the point to obtain the terrain standard deviation;
step 32: assigning a value to the terrain influence coefficient according to the combination condition of the terrain elevation and the terrain standard deviation;
step 33: assigning the influence coefficient of the water system according to the conditions of rivers, lakes and reservoirs in the site;
step 34: obtaining a comprehensive value of the pregnant disaster environment sensitivity according to the terrain influence coefficient and the water system influence coefficient;
step 35: acquiring the distribution condition of the comprehensive disaster-prone environment sensitivity values of each place in the specific area, and giving a comprehensive disaster-prone environment sensitivity value B to each place according to the position of the comprehensive disaster-prone environment sensitivity values of each place in the distribution condition;
and/or
The calculation of vulnerability of the disaster-bearing body comprises the following steps:
counting the power supply quantity and the total power consumption quantity data of counties and cities of each place in the specific area, acquiring the distribution condition of the power supply quantity and the total power consumption quantity data of the counties and cities of each place in the specific area, and giving a vulnerability numerical value C of a disaster-bearing body of each place according to the position of each place in the distribution condition of the counties and cities;
and/or
The calculation of the disaster prevention and resistance capability comprises the following steps:
and counting the GDP data of the prefectures in the counties and cities of each place in the specific area, acquiring the distribution condition of the GDP data of the prefectures in the counties and cities of each place in the specific area, and giving disaster prevention and resistance capacity values D to each place according to the positions of the distribution condition of the prefectures in the counties and cities.
3. The method for evaluating the risk of power grid disaster based on the strong precipitation space-time distribution characteristics as claimed in claim 1,
in the calculation of the risk of the disaster-causing factor, the maximum values of the accumulated rainfall of the heavy rain and the accumulated rainfall of the short-time heavy rainfall are divided into 1-5 grades according to five numerical value ranges of 60% -80%, 80% -90%, 90% -95%, 95% -98% and more than 98%, and coefficients of 1/15, 2/15, 3/15, 4/15 and 5/15 are respectively given;
the percentage values of breakpoints of the rainstorm risk indexes of all the sites in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes are respectively 20%, 40%, 60% and 80%, and the risk values A of the disaster-causing factors of the sites are respectively defined as 1, 2, 3, 4 and 5;
in the calculation of the pregnant disaster environment sensitivity, the combined situation of the terrain elevation and the terrain standard deviation is used for assigning values to the terrain influence coefficients, and the values are respectively as follows:
when the terrain elevation H is less than 100m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m which is less than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d which is more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 100m and less than 300m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 300m and less than 700m, the assignment of a terrain standard deviation grade d <1m is 0.8, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.7, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.6;
when the terrain elevation H is larger than or equal to 700m, the assignment of the terrain standard deviation grade d <1m is 0.7, the assignment of the terrain standard deviation grade d <10m which is smaller than or equal to 1m is 0.6, and the assignment of the terrain standard deviation grade d larger than or equal to 10m is 0.5;
the influence coefficients of the water system are assigned to be within 8km from a first-level river and within 6km from a second-level river according to the conditions of the rivers, the lakes and the reservoirs in the sites, the influence coefficients of the water system are 0.8, the influence coefficients of the water system are 0.4 and the coefficients between the two distances are 0.4-0.8, and the influence coefficients of the water system are beyond 12km from the first-level river and 10km from the second-level river;
adding the two assignments, then averaging, carrying out numerical breakpoint according to the proportion of 20%, 40%, 60% and 80%, and respectively defining the pregnant disaster environment sensitivity values B of the sites as 1, 2, 3, 4 and 5;
in the calculation of the vulnerability of the disaster-bearing body, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of the total data of the power supply quantity and the power consumption quantity in a specific area, and the vulnerability numerical values C of the disaster-bearing body of the site are respectively defined to be 1, 2, 3, 4 and 5;
in the calculation of the disaster prevention and resistance capability, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of GDP data of people in counties and cities of each place in a specific area, and the disaster prevention and resistance capability numerical values D of the places are respectively defined as 1, 2, 3, 4 and 5.
4. The power grid disaster risk assessment method based on the strong precipitation space-time distribution characteristics as claimed in claim 3,
power grid heavy rainfall disaster risk index DR ═ A0.3×B0.3×C0.2×D0.2。
5. The power grid disaster risk assessment method based on the high precipitation spatio-temporal distribution characteristics according to claim 4, wherein the power grid high precipitation disaster risk indexes of the sites correspond to low risk, medium high risk and high risk respectively when the power grid high precipitation disaster risk indexes are less than 20%, 20% to 40%, 40% to 60%, 60% to 80% and more than 80% of the total distribution of the power grid high precipitation disaster risk indexes.
6. A power grid disaster risk assessment device based on strong precipitation space-time distribution characteristics is characterized by comprising,
a risk calculation module of the disaster causing factor: calculating the dangerousness of the disaster causing factor based on the rainfall data of each place in the specific area;
a pregnant disaster environment sensitivity calculation module: calculating the environmental sensitivity of pregnant disaster based on the terrain elevation data of each place;
disaster-bearing body vulnerability calculation module: calculating the vulnerability of the disaster-bearing body based on the power supply amount and the power consumption amount data of each place;
the disaster prevention and resistance calculation module: calculating disaster prevention and resistance capacity based on the per-capita GDP data of counties and cities of each place;
the power grid heavy precipitation disaster risk index calculation module: and obtaining the power grid heavy rainfall disaster risk indexes of different places in each place of the specific area according to the danger of the disaster causing factor, the pregnant disaster environment sensitivity, the vulnerability of a disaster bearing body and the disaster prevention and resistance capability.
7. The power grid disaster risk assessment device based on the strong precipitation space-time distribution characteristics as claimed in claim 6,
the calculation of the risk of the disaster causing factor comprises the following steps:
step 11: carrying out distribution statistics on rainfall and frequency of rainstorms which last for 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 7 days or more in each place of the specific area every year and are rainstorms for at least one day and/or short-time heavy rainfall for at least one hour, and calculating the accumulated rainfall during rainfall according to the rainfall;
step 12: classifying grades according to the accumulated rainfall and endowing each grade with a coefficient;
step 13: multiplying the frequency of rainstorm and/or short-time heavy rainfall corresponding to each grade by the grade coefficient, then adding all the grades to obtain rainstorm risk indexes and/or short-time heavy rainfall risk indexes of different places in a specific area, and carrying out breakpoint according to the rainstorm risk indexes of all the places in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes to endow all the places in the specific area with risk values A of disaster-causing factors;
and/or
The method for calculating the environmental sensitivity of pregnancy disasters comprises the following steps:
step 31: obtaining the terrain elevation of a certain place according to the terrain height data, and calculating the standard deviation of eight adjacent points around the point to obtain the terrain standard deviation;
step 32: assigning a value to the terrain influence coefficient according to the combination condition of the terrain elevation and the terrain standard deviation;
step 33: assigning the influence coefficient of the water system according to the conditions of rivers, lakes and reservoirs in the site;
step 34: obtaining a comprehensive value of the pregnant disaster environment sensitivity according to the terrain influence coefficient and the water system influence coefficient;
step 35: acquiring the distribution condition of the comprehensive disaster-prone environment sensitivity values of each place in the specific area, and giving a comprehensive disaster-prone environment sensitivity value B to each place according to the position of the comprehensive disaster-prone environment sensitivity values of each place in the distribution condition;
and/or
The calculation of vulnerability of the disaster-bearing body comprises the following steps:
counting the power supply quantity and the total power consumption quantity data of counties and cities of each place in the specific area, acquiring the distribution condition of the power supply quantity and the total power consumption quantity data of the counties and cities of each place in the specific area, and giving a vulnerability numerical value C of a disaster-bearing body of each place according to the position of each place in the distribution condition of the counties and cities;
and/or
The calculation of the disaster prevention and resistance capability comprises the following steps:
and counting the GDP data of the prefectures in the counties and cities of each place in the specific area, acquiring the distribution condition of the GDP data of the prefectures in the counties and cities of each place in the specific area, and giving disaster prevention and resistance capacity values D to each place according to the positions of the distribution condition of the prefectures in the counties and cities.
8. The power grid disaster risk assessment device based on the strong precipitation space-time distribution characteristics as claimed in claim 7,
in the calculation of the risk of the disaster-causing factor, the maximum values of the accumulated rainfall of the heavy rain and the accumulated rainfall of the short-time heavy rainfall are divided into 1-5 grades according to five numerical value ranges of 60% -80%, 80% -90%, 90% -95%, 95% -98% and more than 98%, and coefficients of 1/15, 2/15, 3/15, 4/15 and 5/15 are respectively given;
the percentage values of breakpoints of the rainstorm risk indexes of all the sites in the specific area or the end point values at two ends of the average value of the rainstorm risk indexes and the short-time heavy rainfall risk indexes are respectively 20%, 40%, 60% and 80%, and the risk values A of the disaster-causing factors of the sites are respectively defined as 1, 2, 3, 4 and 5;
in the calculation of the pregnant disaster environment sensitivity, the combined situation of the terrain elevation and the terrain standard deviation is used for assigning values to the terrain influence coefficients, and the values are respectively as follows:
when the terrain elevation H is less than 100m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m which is less than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d which is more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 100m and less than 300m, the assignment of a terrain standard deviation grade d <1m is 0.9, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.8, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.7;
when the terrain elevation H is more than or equal to 300m and less than 700m, the assignment of a terrain standard deviation grade d <1m is 0.8, the assignment of a terrain standard deviation grade d <10m more than or equal to 1m is 0.7, and the assignment of a terrain standard deviation grade d more than or equal to 10m is 0.6;
when the terrain elevation H is larger than or equal to 700m, the assignment of the terrain standard deviation grade d <1m is 0.7, the assignment of the terrain standard deviation grade d <10m which is smaller than or equal to 1m is 0.6, and the assignment of the terrain standard deviation grade d larger than or equal to 10m is 0.5;
the influence coefficients of the water system are assigned to be within 8km from a first-level river and within 6km from a second-level river according to the conditions of the rivers, the lakes and the reservoirs in the sites, the influence coefficients of the water system are 0.8, the influence coefficients of the water system are 0.4 and the coefficients between the two distances are 0.4-0.8, and the influence coefficients of the water system are beyond 12km from the first-level river and 10km from the second-level river;
adding the two assignments, then averaging, carrying out numerical breakpoint according to the proportion of 20%, 40%, 60% and 80%, and respectively defining the pregnant disaster environment sensitivity values B of the sites as 1, 2, 3, 4 and 5;
in the calculation of the vulnerability of the disaster-bearing body, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of the total data of the power supply quantity and the power consumption quantity in a specific area, and the vulnerability numerical values C of the disaster-bearing body of the site are respectively defined to be 1, 2, 3, 4 and 5;
in the calculation of the disaster prevention and resistance capability, numerical breakpoints are carried out according to the proportion of 20%, 40%, 60% and 80% of GDP data of people in counties and cities of each place in a specific area, and the disaster prevention and resistance capability numerical values D of the places are respectively defined as 1, 2, 3, 4 and 5.
9. The power grid disaster risk assessment device based on the strong precipitation space-time distribution characteristics as claimed in claim 8,
power grid heavy rainfall disaster risk index DR ═ A0.3×B0.3×C0.2×D0.2。
10. The power grid disaster risk assessment method based on the high precipitation spatio-temporal distribution characteristics according to claim 9, wherein the power grid high precipitation disaster risk indexes of the sites correspond to low risk, medium high risk and high risk respectively when the power grid high precipitation disaster risk indexes are located below 20%, 20% to 40%, 40% to 60%, 60% to 80% and above 80% of the total distribution of the power grid high precipitation disaster risk indexes.
CN201910960260.6A 2019-10-10 2019-10-10 Power grid disaster risk assessment method and device based on strong precipitation space-time distribution characteristics Pending CN110728448A (en)

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Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113177737A (en) * 2021-05-26 2021-07-27 南京恩瑞特实业有限公司 Urban rainstorm disaster risk assessment method and system based on GA (genetic algorithm) optimization BP (back propagation) neural network
CN113869804A (en) * 2021-12-02 2021-12-31 国网江西省电力有限公司电力科学研究院 Power grid equipment risk early warning method and system under flood disaster
CN114580307A (en) * 2022-05-05 2022-06-03 湖南大学 Distributed power supply system electricity-related safety sensing method considering rainstorm space-time distribution

Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN106022953A (en) * 2016-05-13 2016-10-12 国网浙江省电力公司台州供电公司 Power grid infrastructure rainstorm risk assessment method
CN106611245A (en) * 2016-12-21 2017-05-03 国网福建省电力有限公司 GIS-based typhoon disaster risk assessment method for power grid
US20170316357A1 (en) * 2016-04-28 2017-11-02 Honeywell International Inc. Systems and methods for displaying a dynamic risk level indicator of an atm site or other remote monitoring site on a map for improved remote monitoring

Patent Citations (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20170316357A1 (en) * 2016-04-28 2017-11-02 Honeywell International Inc. Systems and methods for displaying a dynamic risk level indicator of an atm site or other remote monitoring site on a map for improved remote monitoring
CN106022953A (en) * 2016-05-13 2016-10-12 国网浙江省电力公司台州供电公司 Power grid infrastructure rainstorm risk assessment method
CN106611245A (en) * 2016-12-21 2017-05-03 国网福建省电力有限公司 GIS-based typhoon disaster risk assessment method for power grid

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
汪小康 等: "河南强降水分布特征及其电网灾害风险区划研究", 《暴雨灾害》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN113177737A (en) * 2021-05-26 2021-07-27 南京恩瑞特实业有限公司 Urban rainstorm disaster risk assessment method and system based on GA (genetic algorithm) optimization BP (back propagation) neural network
CN113869804A (en) * 2021-12-02 2021-12-31 国网江西省电力有限公司电力科学研究院 Power grid equipment risk early warning method and system under flood disaster
CN114580307A (en) * 2022-05-05 2022-06-03 湖南大学 Distributed power supply system electricity-related safety sensing method considering rainstorm space-time distribution
US11821961B2 (en) 2022-05-05 2023-11-21 Hunan University Method for electricity-related security awareness of distributed power supply systems considering spatio-temporal distribution of rainstorms

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