CN108510098B - Power transmission line corridor snow depth estimation and early warning method and system based on satellite remote sensing - Google Patents
Power transmission line corridor snow depth estimation and early warning method and system based on satellite remote sensing Download PDFInfo
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Abstract
The invention discloses a power transmission line corridor snow depth estimation and early warning method and system based on satellite remote sensing, and relates to the field of power transmission line snow depth monitoring. S1, acquiring data and preprocessing the data; s2, acquiring snow depth distribution based on a data assimilation algorithm; s3, acquiring snow depth data distribution of the power transmission line corridor; and S4, early warning of the snow depth of the power transmission line. According to the method, the distribution situation of the snow depth of the power transmission line corridor with higher precision is obtained, early warning is carried out on the snow depth of the power transmission line corridor according to the operation suggestion, the purpose of accurately estimating the snow change of the power transmission line corridor in real time is achieved, and the worst snow and ice situation of the power transmission line is predicted.
Description
Technical Field
The invention relates to the field of monitoring of the snow depth of a power transmission line, in particular to a method and a system for estimating and early warning the snow depth of a power transmission line corridor based on satellite remote sensing.
Background
China is one of the countries with more ice and snow accidents of the transmission line, and the ice and snow accidents seriously threaten the safe operation of the power system in China and cause huge economic loss. The damage of ice and snow to the line comprises overload, ice coating galloping, ice shedding jumping and insulator ice flashing, and the damage can cause accidents such as tower deformation, tower falling, wire strand breaking, hardware and insulator damage, insulator flashover and the like, so that large-area power failure is caused, and the large-area power failure is caused because the accidents frequently occur in severe winter, and the road is frozen and difficult to rush repair, so that long-time power failure is caused. Therefore, the disaster of ice and snow on the transmission line is one of the serious threats faced by the power grid system of many countries.
The influence on the ice and snow disasters of the power transmission line can change along with the change of geography, space and time, and the seasonal effect is strong, the distribution is wide, and the accurate values of all parameters are difficult to obtain. The snow cover and the snow depth are important factors for researching the snow change of the power transmission line corridor, and accurate snow depth monitoring on all the power transmission line corridors is difficult to achieve under the prior art and the economic condition, so how to estimate the snow depth of the power grid power transmission line corridor channel as accurately as possible by combining the means of the prior art and carry out early warning and prompting on the condition possibly influencing the safe operation of the power grid is one of the key problems faced by the safe operation of the power grid.
Disclosure of Invention
The invention aims to provide a power transmission line corridor snow depth estimation and early warning method and system based on satellite remote sensing, so that the problems in the prior art are solved.
In order to achieve the purpose, the invention provides a power transmission line corridor snow depth estimation and early warning method based on satellite remote sensing, which comprises the following steps:
s1, acquiring data of a target power grid, preprocessing the data to obtain MODIS and AMSR-E fused non-cloud snow area data, equal-product projection of meteorological site data and equal-product projection of power grid microclimate site data;
the data comprises meteorological station data, power grid microclimate station data, AMSR-E microwave snow depth data, MODIS snow area data and digital elevation model data of the target power grid;
s2, obtaining the snow depth
S21, establishing virtual stations, establishing the virtual stations in the gridded target power grid, wherein the minimum distance of the known meteorological stations in the target power grid is Q, the minimum distance between the virtual stations and the meteorological stations in the gridded target power grid is Q, and Q is more than or equal to Q;
s22, combining the meteorological station and the virtual station, obtaining the interpolation snow depth and the root mean square error B by using the Kriging interpolation, and converting the spatial resolution of the interpolation snow depth and the root mean square error B into L1Meanwhile, after remote sensing image filtering processing is carried out on the interpolation snow depth by utilizing MODIS and AMSR-E fused cloud-free snow area data, the snow depth of the snow-free pixel is assigned to be 0, and a snow depth predicted value X is obtainedb；
S23, mixing XbThe spatial resolution is L by adopting the average weighting strategy simulation2Simulated snow depth H (X)b)；
S24, using the AMSR-E snow depth data as an observed snow depth value Y0And will observe the snow depth value Y0 ofSpatial resolution conversion to L2Calculating and obtaining the spatial resolution L of the target power grid by using the formula (1) and the formula (2)1The snow depth spatial distribution;
Xa＝Xb+K[Y0-H(Xb)] (1)
K＝BHT(HBHT+R)-1 (2)
wherein: xaThe analysis value of the snow depth of the whole research area at a certain time point; xbThe snow depth is a predicted value, namely the snow depth is interpolated; k is a gain matrix; y isoTo observe the snow depth value; h represents the functional relation between the observed snow depth value and the snow depth predicted value; b is an error covariance matrix of the snow depth predicted value; r is an error covariance matrix of the snow depth observation value; t is the time for acquiring the data of the target power grid;
s3, acquiring the snow depth data distribution of the power transmission line corridor of the target power grid
Inter-resolution of L1The snow depth space is distributed on a GIS platform to be gridded, then all towers in a target power grid are positioned, superposed and gridded on the GIS platform, a grid containing all the towers is selected, all snow depth data outside the selected grid are removed, and the selected grid is subjected to smoothing processing to obtain a power transmission line corridor snow depth distribution space;
s4 early warning of snow depth of any tower
Setting a snow depth increment threshold epsilon and a snow depth threshold h; judging whether the snow depth of any one tower i meets the formula (3), if so, indicating that the snow depth is increased and exceeds a set threshold value, and early warning the tower i; if not, continuing to judge the next tower;
wherein the content of the first and second substances,
represents t0Estimating the snow depth of the tower i at the moment;
indicating that tower i is at t0T adjacent in time1Estimated snow depth at time;
s5, early warning of snow depth of power transmission line
Integrating towers belonging to the same power transmission line to obtain the power transmission line needing early warning, and then early warning the power transmission line needing early warning;
the spatial resolution is L1The value range of (1) is 0-500 m; the spatial resolution is L2The value range of (1) is 0-25 km. .
Preferably, in step S1, the preprocessing is specifically implemented according to the following steps:
s11, respectively performing projection conversion processing on meteorological station data, power grid microclimate station data, AMSR-E microwave snow depth data, MODIS snow area data and digital elevation model data, and converting the data into Lambert azimuth equal-area projection;
s12, splicing and re-projecting the snow cover data in the MODIS snow cover area data after equal-product projection to obtain snow cover data with the spatial resolution of 500 m;
s13, calculating the data of the meteorological sites subjected to equal-product projection and the data of the electric network microclimate sites subjected to equal-product projection to obtain the monthly average snow depth of each meteorological site and each electric network microclimate site;
s14, synthesizing the snow water equivalent in the AMSR-E microwave snow depth data subjected to equal-volume projection with the monthly snow product synthetic data, the near-day analysis data, the SNOWL cloud-removing judgment data and the MODIS snow area data in the MODIS snow area data subjected to equal-volume projection to obtain MODIS and AMSR-E fused non-cloud snow area data.
Preferably, the spatial resolution is L1The value range of (1) is 500 m; the spatial resolution is L2Has a value in the range of 25 km.
The invention discloses a system for realizing a method for estimating and early warning the snow depth of a power transmission line corridor, which comprises the following steps: the device comprises a data acquisition module, a data processing module, an analysis module and an alarm module;
the data acquisition module is used for extracting meteorological station data, power grid microclimate station data, AMSR-E microwave snow depth data, MODIS snow area data and digital elevation model data related to the target power transmission line from a database and preprocessing the obtained data;
the data processing module is used for processing the preprocessed data by adopting a data assimilation algorithm to obtain the target power grid spatial resolution ratio L1The snow depth spatial distribution;
the analysis module: the obtained spatial resolution of the target power grid is L1Carrying out spatial distribution processing on the snow depth spatial distribution to obtain the snow depth distribution on any power transmission line corridor;
the alarm module is: and judging whether the snow depth of any one tower exceeds a preset threshold value, and if so, sending alarm information.
The invention has the beneficial effects that:
aiming at the technical problems in the background technology, the method combines MODIS snow cover data, AMSR-E data, weather monitoring station data and power transmission line corridor microclimate monitoring data, performs spatial interpolation on the maximum snow depth of a power transmission line corridor by using a kriging interpolation method and a data assimilation algorithm in a mode of adding virtual microclimate sites in a snow-free area in the power transmission line corridor to obtain the snow depth distribution condition of the power transmission line corridor with higher precision, performs early warning on the snow depth of the power transmission line corridor according to an operation suggestion, achieves the purpose of accurately estimating the snow change of the power transmission line corridor in real time, and predicts the most unfavorable snow and ice conditions of the power transmission line.
Aiming at the problems of smooth effect generated by actual meteorological data interpolation, uneven distribution of meteorological monitoring points and insufficient remote sensing spatial resolution, the snow cover data are acquired by adopting a medium-high resolution imaging spectrometer MODIS, and are fused with microclimate monitoring data and meteorological monitoring data to obtain non-cloud snow cover area data, and on the basis, a virtual meteorological station is constructed in a power transmission line corridor, so that the defects of few and uneven meteorological stations are overcome. Meanwhile, a data assimilation algorithm is provided to fuse the Kriging space interpolation snow depth based on meteorological observation data, and verification analysis is performed on a fusion result according to the observation data, so that the method can effectively improve the snow depth estimation precision.
Drawings
FIG. 1 is a schematic flow chart of a power transmission line corridor snow depth estimation and early warning method based on satellite remote sensing;
FIG. 2 is a schematic flow chart of the data acquisition and pre-processing of the data in FIG. 1;
FIG. 3 is a schematic flow diagram of the data session algorithm of FIG. 1 for obtaining snow depth distribution;
FIG. 4 is a schematic flow chart of the distribution of the snow depth data of the power transmission line corridor shown in FIG. 1;
fig. 5 is a schematic diagram of a buffer grid used in acquiring a grid where a tower is located in the embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating the invention, are intended for purposes of illustration only and are not intended to limit the scope of the invention.
Taking a certain power-saving company as an example, estimating the snow depth of the power-saving network, and giving an early warning on the snow depth of a certain 220kV transmission line channel, wherein the general flow is shown in fig. 1, and the specific method comprises the following steps:
step 1: data is acquired and pre-processed as shown in fig. 2.
The data comprises meteorological station data, power grid microclimate station data, AMSR-E microwave snow depth data, MODIS snow area data and digital elevation model data.
Firstly, data are acquired, in the embodiment, data of 11 months to 2016 months and 2 months in 2015 of a certain province are selected, and the data specifically comprise:
MODIS snow area data: the daily snow area data MOD10A1, MYD10A1, with a spatial resolution of 500m, contained in MODIS from 11 months in 2015 to 2 months in 2016.
AMSR-E microwave snow depth data: the system comprises AMSR-E day-by-day snow water equivalent data and day-by-day snow depth data, and the spatial resolution is 25 km.
Weather station data: contains data on daily snow depth observations during the period of 33 sites 2015 11 months-2016 months 2.
Grid microclimate site data: the snow depth data measured by 15 microclimate sites comprising 11 power transmission line corridors in total during 11 months from 2015 to 2016 and 2 months.
DEM data: digital Elevation Model (DEM) data including the province-province range, with a resolution of 90 m.
Then, preprocessing the data:
1. firstly, performing unified projection conversion on the data, and converting the data into Lambert azimuth equal-product projection;
2. then splicing and reprojecting the snow cover data of the MODIS by using an MRT tool to obtain snow cover data with the spatial resolution of 500 m;
3. calculating the monthly average snow depth of each station according to the meteorological station data and the power grid micrometeorological station data;
4. and finally, synthesizing MODIS daily snow data, analyzing near days, judging snowL cloud removal, synthesizing MODIS snow area data and AMSR-E snow water equivalent data to obtain MODIS and AMSR-E fused non-cloud snow area data, wherein the spatial resolution is 500 m.
Step 2: the snow depth distribution is obtained based on a data assimilation algorithm, as shown in fig. 3.
1. Establishing a virtual site: firstly, gridding the power-saving network, wherein the size of a grid is 1 degree multiplied by 1 degree; then superposing the 33 meteorological monitoring stations and the 15 power grid micrometeorological monitoring stations with the grid; and selecting a snow-free area according to the cloud-free snow area data fused by the MODIS and the AMSR-E to establish a virtual station.
When the virtual sites are selected, the distances between the virtual sites and the meteorological sites are not smaller than the minimum distance between the existing meteorological sites, and in order to reduce noise errors of the cloud-free snow area data fused by MODIS and AMSR-E as much as possible, a snow-free buffer area is arranged for the virtual sites, the size of the snow-free buffer area is 1 MODIS pixel of 500m, and the whole buffer area is required to have no snow coverage.
Through the process, 23 virtual sites are constructed for the target power grid area.
2. For 71 stations formed by combining the meteorological station and the virtual station, acquiring the snow depth and the root mean square error B by using a Krigin interpolation algorithm, and converting the snow depth difference result into 500m spatial resolution; meanwhile, masking the interpolation snow depth by utilizing MODIS and AMSR-E fused cloud-free snow area data, and assigning the snow depth of the snow-free pixel to be 0 to obtain a snow depth predicted value Xb。
In the embodiment, a height information considered method is adopted, and the formula (1):
in the formula, Z (x)0) Is x0Estimated maximum snow depth in the moon, Z (x)i) Is xiMaximum moon depth observation, λiIs a weight coefficient; y (x) is the elevation at point x; m isy、mzRespectively the global average values of the elevation and the snow depth; n is the number of stations for snow depth interpolation.
The root mean square error B calculation method comprises the following steps:
in the formula, n is the number of microclimate sites;zi,actactually measuring a snow depth value for a meteorological station i; z is a radical ofi,estThe estimated snow depth value for weather station i.
3. Predicting the snow depth XbForward simulation is carried out by adopting an average weighting strategy to obtain a simulated snow depth H (X) with 25km spatial resolutionb)。
4. Taking AMSR-E snow depth data as Y0Spatial resolution 25 km.
By using the parameters and utilizing an assimilation algorithm, multi-source data are fused to obtain snow depth spatial distribution with the spatial resolution of 500 m.
The data assimilation algorithm is as follows:
Xa＝Xb+K[Y0-H(Xb)] (3)
K＝BHT(HBHT+R)-1 (4)
wherein: xaThe analysis value of the snow depth of the whole research area at a certain time point; xbThe predicted value of the snow depth is obtained; k is a gain matrix; y is0To observe the snow depth value; h represents the functional relation between the observed value and the analog value; b is an error covariance matrix of the snow depth predicted value; and R is an error covariance matrix of the snow depth observed value.
And step 3: and (3) distributing the snow depth data of the transmission line corridor as shown in figure 4.
1. Firstly, the snow depth spatial distribution data with the spatial resolution of 500m is gridded on a power grid GIS platform, and then all towers of a power grid are positioned and superposed.
2. And acquiring longitude and latitude information of the tower by using the spatial position service of the power grid GIS platform, and further acquiring the grid of the tower. To ensure the continuity and integrity of the snow depth estimate data, eight grids immediately surrounding the positioning grid are selected as buffer areas, as shown in fig. 5.
3. And then removing the snow depth data of all grids except the selected grid, and smoothing the selected grid by using GIS platform service, so that the spatial distribution of the snow depth of the corridor of the power transmission line can be obtained.
And 4, step 4: and early warning the snow depth of the power transmission line.
According to historical operation experience of the selected target area, accumulated snow in the area is over 30mm, ice coating is possible to occur when the accumulated snow is over 15mm within 12 hours, and therefore the snow depth increment threshold value epsilon is set to 15mm and the snow depth threshold value h is set to 30mm respectively.
In this example, a 220kV transmission line is selected as a test point, three base towers of the lines 22#, 30#, and 45# are selected as examples, and the results of data estimation twice in 11 months and 6 days in 2015 are shown in table 1:
data results of 11/6/12015-year 11/month/6-day two-time estimation
According to the data, the 22# tower can be measured
No alarm is given;
30# Tower
And is
The early warning condition is met;
45# pole tower
And is
And the early warning condition is met.
Furthermore, all towers of the whole 220kV power transmission line are analyzed, and 28# -75# towers meeting early warning conditions are obtained, so that the unified early warning can be carried out on the pole sections.
By adopting the technical scheme disclosed by the invention, the following beneficial effects are obtained:
the method comprises the steps of fusing data such as MODIS, AMSR-E, power grid microclimate monitoring sites, weather monitoring sites and the like, and constructing virtual sites based on the data of the cloud-free snow area fused by MODIS and AMSR-E; then, obtaining snow depth spatial distribution by using a kriging interpolation method and a data assimilation fitting algorithm; then, carrying out spatial superposition with the power transmission line of the power grid to obtain the snow depth spatial distribution of the corridor of the power transmission line; the estimated snow depth of the tower can be obtained according to the positioning information of the tower, and early warning can be performed on the snow depth of the power transmission line corridor by combining with local operation experience. By the method, the power transmission line with the hidden danger of snow accumulation or the danger of ice coating can be timely discovered and intervened as early as possible, and the safe and stable operation of the power system is ensured.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and improvements can be made without departing from the principle of the present invention, and such modifications and improvements should also be considered within the scope of the present invention.
Claims (4)
1. A power transmission line corridor snow depth estimation and early warning method based on satellite remote sensing is characterized by comprising the following steps:
s1, acquiring data of a target power grid, preprocessing the data to obtain MODIS and AMSR-E fused non-cloud snow area data, equal-product projection of meteorological site data and equal-product projection of power grid microclimate site data;
the data comprises meteorological station data, power grid microclimate station data, AMSR-E microwave snow depth data, MODIS snow area data and digital elevation model data of the target power grid;
s2, obtaining the snow depth
S21, establishing virtual stations, establishing the virtual stations in the gridded target power grid, wherein the minimum distance of the known meteorological stations in the target power grid is Q, the minimum distance between the virtual stations and the meteorological stations in the gridded target power grid is Q, and Q is more than or equal to Q;
s22, combining the meteorological station and the virtual station, obtaining the interpolation snow depth and the root mean square error B by using the Kriging interpolation, and converting the spatial resolution of the interpolation snow depth and the root mean square error B into L1All are the same asThen, remote sensing image filtering processing is carried out on the interpolation snow depth by utilizing MODIS and AMSR-E fused cloud-free snow area data, the snow depth of the snow-free pixel is assigned to be 0, and a snow depth predicted value X is obtainedb；
S23, mixing XbThe spatial resolution is L by adopting the average weighting strategy simulation2Simulated snow depth H (X)b)；
S24, using the AMSR-E snow depth data as an observed snow depth value Y0And will observe the snow depth value Y0To L2Calculating and obtaining the spatial resolution L of the target power grid by using the formula (1) and the formula (2)1The snow depth spatial distribution;
Xa＝Xb+K[Y0-H(Xb)] (1)
K＝BHT(HBHT+R)-1 (2)
wherein: xaThe analysis value of the snow depth of the whole research area at a certain time point; xbThe snow depth is a predicted value, namely the snow depth is interpolated; k is a gain matrix; y is0 To observe the snow depth value; h represents the functional relation between the observed snow depth value and the snow depth predicted value; b is an error covariance matrix of the snow depth predicted value; r is an error covariance matrix of the snow depth observation value;
s3, acquiring the snow depth data distribution of the power transmission line corridor of the target power grid
Spatial resolution of L1The snow depth space is distributed on a GIS platform to be gridded, then all towers in a target power grid are positioned, superposed and gridded on the GIS platform, a grid containing all the towers is selected, all snow depth data outside the selected grid are removed, and the selected grid is subjected to smoothing processing to obtain a power transmission line corridor snow depth distribution space;
s4 early warning of snow depth of any tower
Setting a snow depth increment threshold epsilon and a snow depth threshold h; judging whether the snow depth of any one tower i meets the formula (3), if so, indicating that the snow depth is increased and exceeds a set threshold value, and early warning the tower i; if not, continuing to judge the next tower;
wherein the content of the first and second substances,
represents t0Estimating the snow depth of the tower i at the moment;
indicating that tower i is at t0T adjacent in time1Estimated snow depth at time;
s5, early warning of snow depth of power transmission line
Integrating towers belonging to the same power transmission line to obtain the power transmission line needing early warning, and then early warning the power transmission line needing early warning;
the spatial resolution is L1The value range of (1) is 0-500 m; the spatial resolution is L2The value range of (1) is 0-25 km.
2. The method according to claim 1, wherein in step S1, the preprocessing is implemented according to the following steps:
s11, respectively performing projection conversion processing on meteorological station data, power grid microclimate station data, AMSR-E microwave snow depth data, MODIS snow area data and digital elevation model data, and converting the data into Lambert azimuth equal-area projection;
s12, splicing and re-projecting the snow cover data in the MODIS snow cover area data after equal-product projection to obtain snow cover data with the spatial resolution of 500 m;
s13, calculating the data of the meteorological sites subjected to equal-product projection and the data of the electric network microclimate sites subjected to equal-product projection to obtain the monthly average snow depth of each meteorological site and each electric network microclimate site;
s14, synthesizing the snow water equivalent in the AMSR-E microwave snow depth data subjected to equal-volume projection with the monthly snow product synthetic data, the near-day analysis data, the SNOWL cloud-removing judgment data and the MODIS snow area data in the MODIS snow area data subjected to equal-volume projection to obtain MODIS and AMSR-E fused non-cloud snow area data.
3. The method of claim 1, wherein the spatial resolution is L1The value range of (1) is 500 m; the spatial resolution is L2Has a value in the range of 25 km.
4. A system for realizing the satellite remote sensing-based power transmission line corridor snow depth estimation and early warning method as claimed in any one of claims 1-3, wherein the system comprises: the device comprises a data acquisition module, a data processing module, an analysis module and an alarm module;
the data acquisition module is used for extracting meteorological station data, power grid microclimate station data, AMSR-E microwave snow depth data, MODIS snow area data and digital elevation model data related to the target power transmission line from a database and preprocessing the obtained data;
the data processing module is used for processing the preprocessed data by adopting a data assimilation algorithm to obtain the target power grid spatial resolution ratio L1The snow depth spatial distribution;
the analysis module: the obtained spatial resolution of the target power grid is L1Carrying out spatial distribution processing on the snow depth spatial distribution to obtain the snow depth distribution on any power transmission line corridor;
the alarm module is: and judging whether the snow depth of any one tower exceeds a preset threshold value, and if so, sending alarm information.
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| CN103106764B (en) * | 2013-01-11 | 2015-09-02 | 广西电网公司电力科学研究院 | Based on the power transmission line corridor fire monitoring system of satellite remote sensing |
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