CN113240172B - Micro-topography icing numerical prediction method and system - Google Patents
Micro-topography icing numerical prediction method and system Download PDFInfo
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Abstract
The invention discloses a method and a system for predicting a micro-terrain icing value, wherein the method comprises the following steps: classifying the micro-topography according to the line pole tower section to be predicted and the elevation data around the line pole tower section to be predicted; classification includes water body micro-topography and non-water body micro-topography including: canyons, terrain lifting, bealock and mountains; calculating a numerical value calculation result of a kilometer level resolution area by adopting a corresponding numerical mode and a parameter scheme according to the type of the micro-topography, and then interpolating the kilometer level numerical value calculation result into a transition layer to extract a calculated initial field and a calculated boundary field of the micro-topography layer; calculating physical parameters of the micro-topography layer according to the calculated initial field and boundary field; and inputting physical parameters of the micro-topography layer according to the icing model corresponding to the micro-topography category to obtain the icing thickness under the micro-topography of the corresponding type. According to the invention, kilometer level number calculation and calculation fluid are combined, so that accurate prediction of micro-terrain icing is realized.
Description
Technical Field
The invention relates to the technical field of power grid protection, in particular to a micro-topography icing numerical prediction method and system.
Background
In the large background of global warming, the probability of occurrence of global grid ice disasters is increasing, the icing range is increasing, and serious icing begins to occur in non-traditional icing areas of the world. As grid ice resistance levels in non-traditional ice-covered areas tend to be lower, once covered, the losses are more severe. For example: the power grid in texas in the non-icing area of the united states suffers from rare freezing disasters, more than 400 tens of thousands of users have power failure and huge losses in 2021 and 2 months. Serious icing occurs on the lines in North China and northeast China in 2020, wherein about 170 thousands of users in Jilin have power failure, and the power supply line of water and heat supply facilities for 300 thousands of people in Changchun is seriously threatened.
At present, research institutions and meteorological departments develop numerical weather forecast business work for many years, but forecast results are only weather parameters such as precipitation, temperature and the like, the resolution of a calculation grid is 1-3 km, the ice covering scale of micro-terrains is small, the thickness of ice covering inside and outside hundred meters is often different, and the ice covering cannot be forecast by using the weather numerical forecast. For numerical calculation with resolution below kilometer level, a computational fluid model (CFD) method and a laboratory test model are mainly utilized for calculation, but in the calculation process of computational fluid and laboratory model, the dynamic and thermal processes of fluid are mainly considered, the physical change of real atmospheric cloud and mist is lacked, the calculation result deviation is large, and the calculation result deviation cannot be directly used for service prediction, so that the development of the micro-topography icing numerical prediction is very difficult.
Disclosure of Invention
The invention provides a micro-terrain icing numerical prediction method and a micro-terrain icing numerical prediction system, which are used for solving the technical problems that the resolution of a traditional numerical prediction mode is coarse, and the defect of a physical process is not considered in a fluid model calculation.
In order to solve the technical problems, the technical scheme provided by the invention is as follows:
a micro-topography icing numerical prediction method comprises the following steps:
classifying the micro-topography according to the line pole tower section to be predicted and the elevation data around the line pole tower section to be predicted; classification includes water body micro-topography and non-water body micro-topography including: canyons, terrain lifting, bealock and mountains;
calculating a numerical value calculation result of a kilometer level resolution area by adopting a corresponding numerical mode and a parameter scheme according to the type of the micro-topography, and then interpolating the kilometer level numerical value calculation result into a transition layer to extract a calculated initial field and a calculated boundary field of the micro-topography layer;
according to the calculated initial field and boundary field, calculating wind speed V, temperature T, water vapor W and electromagnetic field E of the micro-topography layer;
and inputting the wind speed V, the temperature T, the water vapor W and the electromagnetic field E of the micro-topography layer according to the icing model corresponding to the micro-topography category to obtain the icing thickness under the corresponding micro-topography.
As a further improvement of the method of the invention:
preferably, according to the type of the micro-topography, calculating a numerical calculation result of a region of a kilometer level resolution by adopting a corresponding numerical mode and a parameter scheme, and then interpolating the numerical calculation result of the kilometer level into a transition layer to extract a calculated initial field and a boundary field of the micro-topography layer, comprising the steps of:
setting a minimum resolution calculated in kilometer level, selecting a multi-level amplification coefficient to amplify the resolution for multiple times, and sequentially carrying out multi-layer nesting according to the resolution from large to small after amplification;
setting forecasting time according to the type of the micro-topography of the line tower section to be forecasted, and carrying out integral calculation according to the multi-layer nested multi-level different resolutions to obtain an hour-by-hour numerical calculation result in the future forecasting time of the kilometer level;
setting the resolution of a transition layer, nesting the transition layer into a grid in a layer with the minimum resolution, interpolating the hour-by-hour numerical calculation result onto the grid point of the transition layer, and performing downscaling calculation to obtain the hour-by-hour numerical calculation result in the future forecast time of the transition layer;
setting a region with resolution z in the transition layer, recording the region as a micro-topography layer, and collecting observation data in the micro-topography layer, wherein the observation data comprise temperature, wind speed and cloud parameters; correcting physical parameters of each grid of the micro-topography layer by adopting three-dimensional assimilation, so as to obtain a calculation initial field of the micro-topography layer;
and obtaining the boundary field of the micro-topography layer by interpolation according to the numerical calculation result of the transition layer.
Preferably, classifying the micro-topography comprises firstly judging whether a water body exists in a set range around a line tower section to be predicted, and judging that the water body micro-topography exists; if no water body exists, judging the type of the non-water body micro-topography according to the following table:
table 1 micro-topography classifying and identifying table
Wherein θ H And theta L Included angles between a direction vector M of a line pole tower section M to be predicted and direction vectors H and L of a ridge line and a valley line nearest to the line pole tower section M to be predicted are respectively set; θ α And theta β Threshold values of included angles respectively; satisfy 0<θ α <θ β <Pi/2; sequencing the digital elevations of the set range around M according to the sequencing positions h1 and h2 of the heights of towers at two sides of the M section from large to small; h is a α And h β Respectively, the order threshold values of the heights satisfy 0<h α <h β <100％；
If the non-water body micro-topography and the water body micro-topography are simultaneously met, judging that the composite micro-topography is formed;
the non-water body micro-topography is not satisfied, and the non-water body micro-topography is judged.
Preferably, when the icing thickness under the corresponding type of micro-topography is obtained according to the icing model corresponding to the micro-topography type, if the line tower section to be predicted is the composite micro-topography, the icing thickness is the sum of the icing model calculation result corresponding to the non-water body micro-topography type and the water body micro-topography icing calculation result.
The invention also provides a computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the methods described above when executing the computer program.
The invention has the following beneficial effects:
1. according to the method for predicting the micro-terrain icing numerical value, the micro-terrain is extracted and identified according to the high-resolution digital elevation data, classification is carried out, a corresponding numerical mode and a parameter scheme are adopted to calculate a kilometer-level resolution area according to the type of the micro-terrain, then an initial field and a boundary field calculated by the micro-terrain area are extracted according to a kilometer-level calculation result, and the micro-terrain model is driven to carry out refinement calculation by the initial field and the boundary field, so that accurate prediction of the micro-terrain icing is realized.
2. In the preferred scheme, the micro-terrain icing numerical prediction method skillfully combines kilometer level number calculation and calculation fluid by a layer-by-layer nesting method, thereby not only meeting the physical rationality, but also realizing the high precision of calculation. The method is good in universality and can be used for predicting the micro-topography numerical values of different types in different ice-covered areas worldwide.
3. The computer system can realize automatic calculation of the micro-topography icing and provide support for large-scale development of micro-topography icing prediction.
In addition to the objects, features and advantages described above, the present invention has other objects, features and advantages. The invention will be described in further detail with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a method for predicting micro-terrain icing values in accordance with a preferred embodiment of the present invention;
fig. 2 is a diagram of a nesting of micro-terrain meshes in accordance with a preferred embodiment of the present invention.
Detailed Description
Embodiments of the invention are described in detail below with reference to the attached drawings, but the invention can be implemented in a number of different ways, which are defined and covered by the claims.
Referring to fig. 1, the method for predicting the micro-topography icing value comprises the following steps:
classifying the micro-topography according to the line pole tower section to be predicted and the elevation data around the line pole tower section to be predicted; classification includes water body micro-topography and non-water body micro-topography including: canyons, terrain lifting, bealock and mountains;
calculating a numerical value calculation result of a kilometer level resolution area by adopting a corresponding numerical mode and a parameter scheme according to the type of the micro-topography, and then interpolating the kilometer level numerical value calculation result into a transition layer to extract a calculated initial field and a calculated boundary field of the micro-topography layer;
according to the calculated initial field and boundary field, calculating wind speed V, temperature T, water vapor W and electromagnetic field E of the micro-topography layer;
and inputting the wind speed V, the temperature T, the water vapor W and the electromagnetic field E of the micro-topography layer according to the icing model corresponding to the micro-topography category to obtain the icing thickness under the corresponding micro-topography.
According to the method, micro-terrains are extracted and identified according to high-resolution digital elevation data, classification is carried out, a corresponding numerical mode and a parameter scheme are adopted to calculate a region with kilometer level resolution according to the types of the micro-terrains, then an initial field and a boundary field calculated by the micro-terrains region are extracted according to a kilometer level calculation result, and a micro-topography model is driven to carry out refinement calculation by the initial field and the boundary field, so that accurate prediction of micro-topography icing is achieved.
In practical application, on the basis of the steps, the micro-terrain icing numerical prediction method of the invention can be optimized, and the following examples are shown:
example 1:
the micro-terrain icing numerical prediction method of the embodiment comprises the following steps:
(1) Micro-topography classification:
determining a line pole tower section M to be predicted, extracting ridge lines and valley lines within a range of 10km around the center by adopting ArcGIS according to high-precision digital elevation data, respectively calculating direction vectors H and L of the ridge lines and the valley lines closest to the position M, calculating the included angle between the direction vector M of the M and the ridge lines and the valley lines, and recording as theta H And theta L 。
Sequencing the digital elevations in the range of 10km around M from big to small, obtaining sequencing positions h1 and h2 of the heights of towers at two sides of the M section, and setting a threshold value theta of an included angle α ，θ β Satisfy 0 of<θ α <θ β <Pi/2, setting the order threshold h of the height α ，h β Satisfy 0 of<h α <h β <100, the following classification judgment table is defined:
TABLE 1 non-Water micro-topography classification and identification form
And according to the water system data, calculating whether a water body exists in a range of 10km around the M, and judging that the water body has micro-topography if the water body exists.
The micro-topography result is judged according to the table and the water body:
(a) For satisfying the table definition, the non-water body micro-topography defined by the table definition is mainly used if the water body micro-topography is not satisfied.
(b) And if the table definition is not met, the water body micro-topography is met.
(c) And recording the non-water body micro-topography and the water body micro-topography which simultaneously meet the identification table as composite micro-topography.
(d) For non-water body micro-topography which does not meet the judgment table, the non-water body micro-topography which does not meet the water body micro-topography is all the non-micro-topography.
(2) Kilometer-level resolution numerical calculation:
(2.1) resolution setting:
referring to FIG. 2, for kilometer level calculations, the minimum resolution is set to D, D ε [1km,3km ]]. The amplification factor f is selected to amplify the resolution, for 1 amplification, the resolution is fD, and so on, for k-1 amplification, the resolution is f k D, when f k The value of D is satisfied in [50km,100km ]]And k is the total number of layers calculated in a nested manner, and k is the minimum and is considered to be the optimal amplification. Resolution of D, fD, f for each layer 2 D…f k-1 And D, nesting is carried out sequentially from large to small according to the resolution. The calculation range for each layer may be set according to actual settings, but is generally set to not less than 300×300km for the outermost calculation range.
(2.2) numerical calculation:
setting a physical parameter scheme according to the micro-terrain type selected in the step (1), setting the forecasting time to be 72 hours, and then starting integral calculation according to the resolution set in the step (2.1) to obtain a numerical calculation result of 72 hours, kilometer level and hour by hour in the future.
(3) And (3) calculating a transition layer:
the resolution d of the transition layer is set to satisfy that d is more than or equal to 100m and less than 1000m. Nesting the transition layer into the grid with the resolution of D in the step (2). And (3) interpolating the kilometer resolution prediction result calculated in the step (2.2) onto the boundary of the transition layer, simultaneously interpolating the calculation result at the initial moment onto grid points of the transition layer, setting parameters, and performing downscaling calculation to obtain the calculation result of the transition layer, which is 72 hours in the future.
(4) Micro-topography icing numerical prediction calculation
(4.1) resolution setting
And (3) setting a region with the resolution of z in the transition layer in the step (3) by taking M as a center, recording the region as a micro-topography layer, enabling the resolution to be more than or equal to 30M and less than 100M, and taking the layer for grid division.
(4.2) initial field data correction:
and collecting observation data in the micro-topography layer, including temperature, wind speed, cloud parameters and the like. And correcting the physical parameters of each grid of the micro-topography layer by adopting a three-dimensional assimilation method, thereby obtaining the calculated initial field of the micro-topography layer.
(4.3) micro-topography icing calculation:
and (3) obtaining a boundary field of the micro-topography layer through interpolation according to the calculation result of the transition layer in the step (3). According to the calculated initial field and boundary field, adopting a fluid calculating method to calculate parameters such as wind speed V, temperature T, water vapor W, electromagnetic field E and the like of the micro-topography layer.
According to the micro-topography types divided in the step (1), physical parameters such as wind speed V, temperature T, water vapor W, electromagnetic field E and the like of a micro-topography layer are input into the icing models of different single-type micro-topography to obtain the icing thickness under the corresponding type of micro-topography.
I 1 ＝F 1 (V，T，W，E)
I 2 ＝F 2 (V，T，W，E)
I 3 ＝F 3 (V，T，W，E)
I 4 ＝F 4 (V，T，W，E)
I 5 ＝F 5 (V，T，W，E)
Wherein I is 1 、I 2 、I 3 、I 4 、I 5 Represents the thickness of ice coating, F 1 For the micro-topography icing model of the puerto, F 2 Ice coating model for canyon microtopography, F 3 Is an icing model of mountain micro-topography, F 4 For the topography lifting micro-topography icing model, F 5 Ice coating model for water body micro-topography。
(a) When the model is of a single micro-topography type, the thickness of the ice coating is the calculated result of the ice coating model of the corresponding micro-topography type.
(b) And when the composite micro-terrain type is adopted, the icing thickness is the sum of the icing model calculation result corresponding to the micro-terrain type and the water body micro-terrain icing calculation result.
Example 2:
taking a certain 500kV icing micro-terrain section as an example, carrying out micro-terrain icing numerical prediction, and comprising the following steps:
(1) Micro-topography classification:
determining a line pole tower section M to be predicted, extracting ridge lines and valley lines within a range of 10km around the center by adopting ArcGIS according to high-precision digital elevation data, respectively calculating direction vectors H and L of the ridge lines and the valley lines closest to the position M, calculating the included angle between the direction vector M of the M and the ridge lines and the valley lines, and recording as theta H =pi/3 and θ L ＝π/3。
The digital elevations in the range of 10km around M are ordered from big to small, the ordered positions of 85 percent and 90 percent of the heights of the towers at the two sides of the M section are obtained, and the threshold value theta of the included angle is set α ＝π/12，θ β =5pi/12, satisfy 0<θ α <θ β <Pi/2, setting the order threshold h of the height α ＝20％，h β =80%, satisfy 0<h α <h β <100%, according to the classification and identification table, the micro-topography of the bealock is met. According to the water system data, calculating that no water exists in the range of 10km around M, so that the water system is a micro-terrain of the bealock.
(2) Kilometer-level resolution numerical calculation:
(2.1) resolution setting:
for the calculation of kilometer level, the minimum resolution is set to 1km, the amplification factor f=3 is selected, the resolutions are amplified in turn, when k-1 is 4, the resolution is 81km, the total number of nested layers is 5 layers between 50km and 100km, the resolution of each layer is 1km,3km,9km,27km,81km, and for the outermost layer calculation is set to 324×324km.
(2.2) numerical calculation:
setting a physical parameter scheme according to the micro-topography type selected in the step (1), setting the forecasting time to be 72 hours, and then starting integral calculation according to the resolution set in the step (2.1) to obtain a numerical calculation result of 72 hours in the future and hour by hour.
(3) And (3) calculating a transition layer:
the resolution of the transition layer is set to be 200m, and d is more than or equal to 100m and less than 1000m. And (3) nesting the transition layer into the grid with the resolution of 1km in the step (2). And (3) interpolating the kilometer resolution prediction result calculated in the step (2.2) onto the boundary of the transition layer, simultaneously interpolating the calculation result at the initial moment onto grid points of the transition layer, setting parameters, and performing downscaling calculation to obtain the calculation result of 72 hours in the future.
(4) And (3) predicting and calculating the micro-topography icing value:
(4.1) resolution setting
And (3) setting an area with the resolution of 50M in the transition layer in the step (3) by taking M as a center, recording the area as a micro-topography layer, enabling the resolution to meet the condition that z is more than or equal to 30M and less than 100M, and taking the layer for grid division.
(4.2) initial field data correction:
and collecting observation data in the micro-topography layer, including temperature, wind speed, cloud parameters and the like. And correcting the physical parameters of each grid of the micro-topography layer by adopting a three-dimensional assimilation method, thereby obtaining the calculated initial field of the micro-topography layer.
(4.3) micro-topography icing calculation:
and (3) obtaining a boundary field of the micro-topography layer through interpolation according to the calculation result of the transition layer in the step (3). And calculating parameters such as wind speed V, temperature T, water vapor W, electromagnetic field E and the like of the micro-topography layer by adopting a fluid calculating method. An icing model F for inputting physical parameters into the micro-topography of the bealock 1 And obtaining the thickness of the ice coating under the corresponding type of micro-topography.
I 1 ＝F 1 (V，T，W，E)
Example 3:
taking a certain 220kV icing micro-terrain section as an example, carrying out micro-terrain icing numerical prediction, and comprising the following steps:
(1) Micro-topography classification:
determining a line pole tower section M to be predicted, extracting ridge lines and valley lines within a range of 10km around the center by adopting ArcGIS according to high-precision digital elevation data, respectively calculating direction vectors H and L of the ridge lines and the valley lines closest to the position M, calculating the included angle between the direction vector M of the M and the ridge lines and the valley lines, and recording as theta H =pi/15 and θ L ＝π/15。
The digital elevations in the range of 10km around M are ordered from big to small, the ordered positions of 85 percent and 90 percent of the heights of the towers at the two sides of the M section are obtained, and the threshold value theta of the included angle is set α ＝π/12，θ β =5pi/12, satisfy 0<θ α <θ β <Pi/2, setting the order threshold h of the height α ＝20％，h β =80%, satisfy 0<h α <h β <100%, according to the classification and identification table, the micro-topography of the bealock is met. According to the water system data, calculating that water exists in a range of 10km around M, so that the water is a composite micro-topography of 'mountain + water'.
Steps (2) to (4.2) are the same as in example 2:
(4.3) micro-topography icing calculation:
and (3) obtaining a boundary field of the micro-topography layer through interpolation according to the calculation result of the transition layer in the step (3). And calculating parameters such as wind speed V, temperature T, water vapor W, electromagnetic field E and the like of the micro-topography layer by adopting a fluid calculating method. Inputting physical parameters into an icing model F of mountain micro-topography 3 And obtaining the thickness of the ice coating under the corresponding type of micro-topography.
I 3 ＝F 3 (V，T，W，E)
I 5 ＝F 5 (V，T，W，E)
The final ice coating thickness is I 3 +I 5 。
Example 4:
the present embodiment provides a computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, the processor implementing the steps of any of the embodiments described above when executing the computer program.
In summary, the invention extracts and identifies the micro-topography according to the high-resolution digital elevation data, classifies the micro-topography, calculates the kilometer resolution area according to the type of the micro-topography by adopting a corresponding numerical mode and a parameter scheme, extracts the initial field and the boundary field calculated by the micro-topography area according to the kilometer calculation result, and drives the micro-topography model to refine calculation by utilizing the initial field and the boundary field, thereby realizing the accurate prediction of the micro-topography icing. And by means of a layer-by-layer nesting method, kilometer level value calculation and calculation fluid are combined skillfully, physical rationality is met, and high calculation accuracy is realized. The method is good in universality and can be used for predicting the micro-topography numerical values of different types in different ice-covered areas worldwide. The automatic calculation of the micro-topography icing can be realized, and support is provided for large-scale development of micro-topography icing prediction.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (4)
1. The method for predicting the micro-terrain icing numerical value is characterized by comprising the following steps of:
classifying the micro-topography according to the line pole tower section to be predicted and the elevation data around the line pole tower section to be predicted; classification includes water body micro-topography and non-water body micro-topography including: canyons, terrain lifting, bealock and mountains;
calculating a numerical value calculation result of a kilometer level resolution area by adopting a corresponding numerical mode and a parameter scheme according to the type of the micro-topography, and then interpolating the kilometer level numerical value calculation result into a transition layer to extract a calculated initial field and a calculated boundary field of the micro-topography layer; the method comprises the following steps:
setting a minimum resolution calculated in kilometer level, selecting a multi-level amplification coefficient to amplify the resolution for multiple times, and sequentially carrying out multi-layer nesting according to the resolution from large to small after amplification;
setting forecasting time according to the type of the micro-topography of the line tower section to be forecasted, and carrying out integral calculation according to the multi-layer nested multi-level different resolutions to obtain an hour-by-hour numerical calculation result in the future forecasting time of the kilometer level;
setting the resolution of a transition layer, nesting the transition layer into a grid in a layer with the minimum resolution, interpolating the hour-by-hour numerical calculation result onto grid points of the transition layer, and performing downscaling calculation to obtain the hour-by-hour numerical calculation result in the future forecast time of the transition layer;
setting a region with resolution z in the transition layer, recording the region as a micro-topography layer, and collecting observation data in the micro-topography layer, wherein the observation data comprise temperature, wind speed and cloud parameters; correcting physical parameters of each grid of the micro-topography layer by adopting three-dimensional assimilation, so as to obtain a calculation initial field of the micro-topography layer;
obtaining a boundary field of the micro-topography layer through interpolation according to the numerical calculation result of the transition layer;
according to the calculated initial field and boundary field, calculating the wind speed V, the temperature T, the water vapor W and the electromagnetic field E of the micro-topography layer;
and inputting the wind speed V, the temperature T, the water vapor W and the electromagnetic field E of the micro-topography layer according to the icing model corresponding to the micro-topography category to obtain the icing thickness under the corresponding micro-topography.
2. The method for predicting the icing value of the micro-topography according to claim 1, wherein the classifying the micro-topography comprises firstly judging whether a water body exists in a set range around a line tower section to be predicted, and judging that the water body has the micro-topography if the water body exists; if no water body exists, judging the type of the non-water body micro-topography according to the following table:
table 1 micro-topography classifying and identifying table
Wherein θ H And theta L Included angles between a direction vector M of a line pole tower section M to be predicted and direction vectors H and L of a ridge line and a valley line nearest to the line pole tower section M to be predicted are respectively set; θ α And theta β Threshold values of included angles respectively; satisfy 0<θ α <θ β <Pi/2; sequencing the digital elevations of the set range around M according to the sequencing positions h1 and h2 of the heights of towers at two sides of the M section from large to small; h is a α And h β Respectively, the order threshold values of the heights satisfy 0<h α <h β <100％；
If the non-water body micro-topography and the water body micro-topography are simultaneously met, judging that the composite micro-topography is formed;
the non-water body micro-topography is not satisfied, and the non-water body micro-topography is judged.
3. The method for predicting the icing value of the micro-terrain according to claim 2, wherein when the icing thickness under the corresponding type of micro-terrain is obtained according to the icing model corresponding to the micro-terrain type, if the section of the line tower to be predicted is the composite micro-terrain, the icing thickness is the sum of the icing model calculation result corresponding to the non-water micro-terrain type and the water micro-terrain icing calculation result.
4. A computer system comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the steps of the method of any of the preceding claims 1 to 3 when the computer program is executed.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013181463A1 (en) * | 2012-05-30 | 2013-12-05 | Saint Louis University | Apparatus and method for provoding environmental predictive indicators to emergency response managers |
| CN103673960A (en) * | 2012-08-30 | 2014-03-26 | 国际商业机器公司 | Method and device for predicating icing state of power transmission line |
| CN110210002A (en) * | 2019-05-21 | 2019-09-06 | 国网湖北省电力有限公司 | A kind of ice covering on transmission lines warning algorithm |
| CN110705796A (en) * | 2019-10-09 | 2020-01-17 | 国网湖南省电力有限公司 | Magnitude frequency correction ensemble forecasting method and system for power grid rainstorm numerical forecasting |
| CN112182823A (en) * | 2020-10-22 | 2021-01-05 | 国网湖南省电力有限公司 | Automatic identification method and system for icing microtopography based on vector calculation |
| CN112711919A (en) * | 2020-12-17 | 2021-04-27 | 国网湖南省电力有限公司 | Conductor icing forecasting method and system based on middle and small scale mode coupling |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101966639B1 (en) * | 2018-04-19 | 2019-07-26 | 대한민국 | Apparatus for forecasting of hydrometeor classification using numerical weather prediction model and method thereof |
-
2021
- 2021-05-11 CN CN202110510551.2A patent/CN113240172B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2013181463A1 (en) * | 2012-05-30 | 2013-12-05 | Saint Louis University | Apparatus and method for provoding environmental predictive indicators to emergency response managers |
| CN103673960A (en) * | 2012-08-30 | 2014-03-26 | 国际商业机器公司 | Method and device for predicating icing state of power transmission line |
| CN110210002A (en) * | 2019-05-21 | 2019-09-06 | 国网湖北省电力有限公司 | A kind of ice covering on transmission lines warning algorithm |
| CN110705796A (en) * | 2019-10-09 | 2020-01-17 | 国网湖南省电力有限公司 | Magnitude frequency correction ensemble forecasting method and system for power grid rainstorm numerical forecasting |
| CN112182823A (en) * | 2020-10-22 | 2021-01-05 | 国网湖南省电力有限公司 | Automatic identification method and system for icing microtopography based on vector calculation |
| CN112711919A (en) * | 2020-12-17 | 2021-04-27 | 国网湖南省电力有限公司 | Conductor icing forecasting method and system based on middle and small scale mode coupling |
Non-Patent Citations (2)
| Title |
|---|
| 基于微地形的输电线路覆冰等级判别方法;陆佳政 等;电力科学与技术学报;第28卷(第04期);第24-30页 * |
| 架空输电线路覆冰厚度预测技术研究;黄俊杰 等;湖北电力;第40卷(第12期);第1-4,24页 * |
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