CN116167211A - Wind field prediction system and method for complex terrain area of power grid power transmission channel - Google Patents

Wind field prediction system and method for complex terrain area of power grid power transmission channel Download PDF

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CN116167211A
CN116167211A CN202310025361.0A CN202310025361A CN116167211A CN 116167211 A CN116167211 A CN 116167211A CN 202310025361 A CN202310025361 A CN 202310025361A CN 116167211 A CN116167211 A CN 116167211A
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章涵
李健
谷山强
苏杰
邵先军
吴敏
王少华
雷梦飞
王振国
李涛
李特
姜云土
任华
姜凯华
陶瑞祥
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Wuhan NARI Ltd
State Grid Zhejiang Electric Power Co Ltd
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
State Grid Electric Power Research Institute
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State Grid Zhejiang Electric Power Co Ltd
NARI Group Corp
Electric Power Research Institute of State Grid Zhejiang Electric Power Co Ltd
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Abstract

The invention discloses a wind field prediction system and a wind field prediction method for a complex terrain area of a power grid power transmission channel, wherein historical observation data and pattern grid point analysis data of a selected area of the power transmission channel are collected, and historical data for prediction are obtained; acquiring site observation data variables of a prediction area; acquiring wind field prediction coefficients based on the historical observation data and site observation data variables; wind farms are predicted based on site observation data variables and wind farm prediction coefficients. The method for predicting the wind field of the complex terrain of the power grid power transmission channel comprises the main factors of influence of the complex terrain, the near-ground wind field and the temperature field. The method establishes the data set to realize region division and time division with stronger practicability, the established prediction equation relies on wind field, temperature and air pressure data of the power grid transmission channel in the past year, and on the basis of defining main factors of complex terrains, near-ground wind fields and temperature fields, powerful data support is provided for the prediction equation by utilizing grid point re-analysis data, and the accuracy of the prediction equation is greatly improved.

Description

Wind field prediction system and method for complex terrain area of power grid power transmission channel
Technical Field
The invention relates to a wind field prediction method in the meteorological field, in particular to a wind field prediction method for a complex terrain area of a power transmission channel.
Background
With the development of supercomputers and the gradual maturity of numerical weather forecast technologies, a numerical weather forecast mode is applied to weather service as an important forecast means, so that more accurate weather element forecast can be provided for weather, climate, water conservancy, electric power and other applications. However, in the solving process of the numerical weather forecast, the numerical weather forecast mode has initial uncertainty and mode uncertainty due to the imperfections and model defects of initial conditions, and the uncertainty can restrict forecast skills. The terrain in the power transmission channel area is complex, and the prediction error of the power transmission channel area is large because the mode is insufficient for describing the terrain and the observation data of the complex terrain cannot be fully utilized. Moreover, it is difficult and heavy to combine weather systems of different scales with such complex steep terrains, so that it is not easy to make predictions of these areas in numerical prediction modes, which involves on the one hand complex terrain processing problems and on the other hand data processing problems on complex terrains.
The existing wind field prediction method is mainly aimed at the meteorological field, for example: the evolution of the similar example is directly taken as the basis of the current prediction or the numerical solution model is directly established through discretization of a dynamic equation. The method is less in application to complicated topography areas of the power grid, and influences of complicated topography of a power grid transmission channel on prediction are not fully considered.
Disclosure of Invention
The invention aims to provide a wind field prediction system and a wind field prediction method for a power transmission channel of a power grid, which creatively provides a wind field prediction method for the power transmission channel, wherein the wind field prediction system is used for collecting site observation data and mode analysis data of the power transmission channel of the power grid in the past year for analysis, and the wind field and the temperature field of the power transmission channel of the power grid are the main factors of the actual wind field of the power transmission channel affected by the complex terrain and the near-ground wind field.
The wind field prediction system for the complex terrain area of the power transmission channel of the power grid comprises a data collection processing module, a prediction area data acquisition module, a wind field prediction coefficient acquisition module and a wind field prediction module; the data collection processing module is used for collecting historical observation data and mode lattice point analysis data of a selected area of the power transmission channel and obtaining historical data for prediction; the prediction area data acquisition module is used for acquiring site observation data variables of each automatic meteorological station in the selected area of the power transmission channel; the wind field prediction coefficient acquisition module acquires a wind field prediction coefficient based on historical observation data of the collection processing module and site observation data variables of each automatic meteorological station in the selected region of the power transmission channel; the wind field prediction module predicts a wind field based on site observation data variables of each automatic meteorological station in the selected region of the power transmission channel and the wind field prediction coefficients acquired by the wind field prediction coefficient acquisition module.
The specific implementation method of the data collection processing module comprises the following steps:
s110, dividing longitude and latitude grids of a selected area by a preset distance according to a power transmission channel, and dividing the natural year by a preset time interval;
s120, collecting historical observation data and mode grid point analysis data of the selected region of the power transmission channel for p years, wherein p is preferably 10 years in the embodiment;
s130, interpolating the mode grid point analysis data in each preset time interval to each automatic weather station site position in the historical observation data by using an interpolation algorithm; and analyzing the data and the history with the interpolated pattern grid pointsThe observation data establishes an interpolated historical analysis observation data set and an interpolated grid point data set of each site position of the automatic weather station and each preset time interval delta t; the historical analysis observation data set is Z ij The interpolated lattice point observation data is G ij, wherein ,Zij Site observations representing the ith site location, the jth predetermined time interval, G ij Interpolated grid point observation data representing an ith site location at a jth predetermined time interval;
s140, re-analyzing the data set G for the interpolated lattice ij ' normalization processing is carried out to obtain a normalized data set
Figure BDA0004044349360000021
The formula of the normalization process is as follows:
Figure BDA0004044349360000031
the historical observation data comprises the ground automatic weather station 10 m wind direction, the ground automatic weather station 10 m wind speed, the ground automatic weather station 2 m temperature and the ground automatic weather station air pressure observed by the automatic weather station in each preset time interval on the power transmission channel;
the mode grid point analysis data comprise 10 m wind direction of the mode analysis ground, 10 m wind speed of the mode analysis ground, 2 m temperature of the mode analysis ground and the mode analysis ground air pressure in each longitude and latitude grid, wherein the minimum distance of the longitude and latitude grid is the horizontal resolution of the grid point analysis data.
The specific implementation mode of the interpolation algorithm is as follows:
if the site position distance is greater than or equal to the horizontal resolution of the lattice point analysis data, selecting the interpolation algorithm as the nearest neighbor method; if the site position distance is smaller than the horizontal resolution of the lattice point re-analysis data, the interpolation algorithm is selected as a bilinear interpolation method, and the bilinear interpolation formula is as follows:
Figure BDA0004044349360000032
wherein Zij The position of the ith station is (x, y), and the adjacent four grid points are respectively analyzed to be data
Figure BDA0004044349360000033
Said->
Figure BDA0004044349360000034
Is (x) 1 ,y 1 ) Said->
Figure BDA0004044349360000035
Is (x) 2 ,y 1 ) Said->
Figure BDA0004044349360000036
Is (x) 1 ,y 2 ) Said->
Figure BDA0004044349360000037
Is (x) 2 ,y 2 )；
The interpolated lattice re-analysis data set G ij ' including interpolated 10 meters wind direction G ij (wdir), interpolated 10 m wind speed G ij (spd), interpolated 2 m temperature G ij (T) and the interpolated ground air pressure G ij (P)。
The specific implementation method of the prediction area data acquisition module comprises the following steps:
acquiring site observation data variables of each automatic weather station in the selected area of the power transmission channel, wherein the site observation data variables of each automatic weather station in the selected area of the power transmission channel comprise weft wind
Figure BDA0004044349360000041
Wind in the channel->
Figure BDA0004044349360000042
And ground altitude->
Figure BDA0004044349360000043
Where i represents the ith site location, τ k And the historical time of the kth time before the predicted time acquired according to the preset time interval is represented, delta t represents the preset time interval, and the historical time of the kth time is the predicted time and is pushed forward for delta t multiplied by k hours.
The specific implementation method of the wind field prediction coefficient acquisition module comprises the following steps:
s310, estimating a weft wind prediction initial guess value CFu by using site observation data variables of selected areas of power transmission channels i (t) and preliminary guess for warp wind prediction CFv i (t), wherein i represents the i-th site location, and t represents the predicted time; the estimation formula is as follows:
Figure BDA0004044349360000044
Figure BDA0004044349360000045
wherein ,
Figure BDA0004044349360000046
the historical moment weft wind of the 1 st time before the time is predicted for the ith site position,
Figure BDA0004044349360000047
predicting the historical time latitudinal wind of the (k+1) th time before the time for the (i) th site position,/for the (i) th site position>
Figure BDA0004044349360000048
Predicting the historical moment latitudinal wind of the kth time before the time for the ith site location,/>
Figure BDA0004044349360000049
Predicting the time before for the ith station position +.>
Figure BDA00040443493600000410
Weft wind at historical time of each time, < ->
Figure BDA00040443493600000411
The historical time of the 1 st time before the forecast time for the ith site location is windward,/>
Figure BDA00040443493600000412
Predicting the historical moment of the (k+1) th time before the time for the (i) th site position by wind,/wind>
Figure BDA00040443493600000413
Predicting the historical moment of the kth time before the time for the ith site location is windward,/>
Figure BDA00040443493600000414
Predicting the time before for the ith station position +.>
Figure BDA00040443493600000415
The history of the time passes into the wind, k represents the ordinal number of the time before the predicted time, +.>
Figure BDA00040443493600000416
Figure BDA00040443493600000417
Representing an upward rounding;
s320, calculating the comprehensive distance between the wind field prediction initial guess value and all the historical analysis grid point data
Figure BDA0004044349360000051
Figure BDA0004044349360000052
in the formula ,
Figure BDA0004044349360000053
the value of the nth variable, which represents the normalization of the ith station position and the jth-k predetermined time interval, where k represents the ordinal number of the time preceding the predicted time,/->
Figure BDA0004044349360000054
Represents an upward rounding, n represents the ordinal number of the four variables, σ n Weights for the nth variable;
S330, calculating the comprehensive distance S between the initial vector of weft wind and the initial vector of warp wind and all historical analysis lattice point data ij ：
Figure BDA0004044349360000055
S340, calculating wind field prediction coefficients and utilizing ground altitude
Figure BDA0004044349360000056
Calculating a predicted estimated total number:
Figure BDA0004044349360000057
wherein
Figure BDA0004044349360000058
Representing an upward rounding;
by using the integrated distance S ij Selecting the minimum M distances to construct a nearest distance vector Smin im Where i denotes the i-th site position, M denotes the ordinal number of the nearest distance, m= … M; calculating wind field prediction coefficient omega ij ：
Figure BDA0004044349360000059
The n=1 represents the variable ground 10 m wind direction, n=2 represents the variable 10 m wind speed, n=3 represents the variable 2 m temperature, and n=4 represents the variable ground air pressure.
The specific implementation method of the wind field prediction module 4 is as follows:
observing a data set Z using the historical analysis ij Is at a wind speed Z of 10 meters ij (wsp) and 10 m wind direction observations Z ij (wdir) calculating historically observed weft wind Zu ij And warp direction wind Zv ij The calculation formula is as follows:
Zu ij ＝-Z ij (wsp)×sin(Z ij (wdir))
Zv ij ＝-Z ij (wsp)×cos(Z ij (wdir))
calculating predicted weft wind Fu i And warp direction Fv i The calculation formula is as follows:
Figure BDA0004044349360000061
Figure BDA0004044349360000062
where i represents the ith site location, ω ij And predicting coefficients for the wind field.
A wind field prediction method for a complex terrain area of a power grid power transmission channel comprises the following steps:
step 1, collecting historical observation data and mode lattice point analysis data of a selected area of a power transmission channel, and obtaining historical data for prediction;
Step 2, acquiring site observation data variables of each automatic meteorological station in a selected area of a power transmission channel;
step 3, wind field prediction coefficients are obtained based on the historical observation data and site observation data variables of each automatic meteorological station in the selected region of the power transmission channel;
and 4, predicting a wind field based on site observation data variables of each automatic meteorological station in the selected area of the power transmission channel and the wind field prediction coefficients.
The specific implementation method of the step 1 is as follows:
s110, dividing longitude and latitude grids of a selected area by a preset distance according to a power transmission channel, and dividing the natural year by a preset time interval;
s120, collecting historical observation data and mode grid point analysis data of the selected region of the power transmission channel for p years, wherein p is preferably 10 years in the embodiment;
s130, interpolating the mode grid point analysis data in each preset time interval to each automatic weather station site position in the historical observation data by using an interpolation algorithm; establishing a post-interpolation historical analysis observation data set and an post-interpolation grid point data set of each automatic weather station site position and each preset time interval delta t according to the post-interpolation mode grid point analysis data and the historical observation data; the historical analysis observation data set is Z ij The interpolated lattice point observation data is G ij, wherein ,Zij Site observations representing the ith site location, the jth predetermined time interval, G ij Interpolated grid point observation data representing an ith site location at a jth predetermined time interval;
s140, re-analyzing the data set G for the interpolated lattice ij ' normalization processing is carried out to obtain a normalized data set
Figure BDA0004044349360000071
The formula of the normalization process is as follows:
Figure BDA0004044349360000072
the historical observation data comprises the ground automatic weather station 10 m wind direction, the ground automatic weather station 10 m wind speed, the ground automatic weather station 2 m temperature and the ground automatic weather station air pressure observed by the automatic weather station in each preset time interval on the power transmission channel;
the mode grid point analysis data comprise 10 m wind direction of a mode analysis ground, 10 m wind speed of the mode analysis ground, 2 m temperature of the mode analysis ground and mode analysis ground air pressure in each longitude and latitude grid, wherein the minimum distance of the longitude and latitude grid is the horizontal resolution of the grid point analysis data;
the specific implementation manner of the interpolation algorithm in the step S130 is as follows:
if the site position distance is greater than or equal to the horizontal resolution of the lattice point analysis data, selecting the interpolation algorithm as the nearest neighbor method; if the site position distance is smaller than the horizontal resolution of the lattice point re-analysis data, the interpolation algorithm is selected as a bilinear interpolation method, and the bilinear interpolation formula is as follows:
Figure BDA0004044349360000073
wherein Zij The position of the ith station is (x, y), and the adjacent four grid points are respectively analyzed to be data
Figure BDA0004044349360000074
Said->
Figure BDA0004044349360000075
Is (x) 1 ,y 1 ) Said->
Figure BDA0004044349360000076
Is (x) 2 ,y 1 ) Said->
Figure BDA0004044349360000077
Is (x) 1 ,y 2 ) Said->
Figure BDA0004044349360000078
Is (x) 2 ,y 2 )；
The interpolated lattice re-analysis data set G ij ' including interpolated 10 meters wind direction G ij (wdir) interpolationThe wind speed G of 10 meters ij (spd), interpolated 2 m temperature G ij (T) and the interpolated ground air pressure G ij (P)；
The specific implementation method of the step 2 is as follows:
acquiring site observation data variables of each automatic weather station in the selected area of the power transmission channel, wherein the site observation data variables of each automatic weather station in the selected area of the power transmission channel comprise weft wind
Figure BDA0004044349360000081
Wind in the channel->
Figure BDA0004044349360000082
And ground altitude->
Figure BDA0004044349360000083
Where i represents the ith site location, τ k And the historical time of the kth time before the predicted time acquired according to the preset time interval is represented, delta t represents the preset time interval, and the historical time of the kth time is the predicted time and is pushed forward for delta t multiplied by k hours.
The specific implementation method of the step 3 is as follows:
s310, estimating a weft wind prediction initial guess value CFu by using site observation data variables of selected areas of power transmission channels i (t) and preliminary guess for warp wind prediction CFv i (t), wherein i represents the i-th site location, and t represents the predicted time; the estimation formula is as follows:
Figure BDA0004044349360000084
Figure BDA0004044349360000085
wherein ,
Figure BDA0004044349360000086
when predicting for the ith site locationThe 1 st time before the time is in the weft direction,
Figure BDA0004044349360000087
predicting the historical time latitudinal wind of the (k+1) th time before the time for the (i) th site position,/for the (i) th site position>
Figure BDA0004044349360000088
Predicting the historical moment latitudinal wind of the kth time before the time for the ith site location,/>
Figure BDA0004044349360000089
Predicting the time before for the ith station position +.>
Figure BDA00040443493600000810
Weft wind at historical time of each time, < ->
Figure BDA00040443493600000811
The historical time of the 1 st time before the forecast time for the ith site location is windward,/>
Figure BDA00040443493600000812
Predicting the historical moment of the (k+1) th time before the time for the (i) th site position by wind,/wind>
Figure BDA00040443493600000813
Predicting the historical moment of the kth time before the time for the ith site location is windward,/>
Figure BDA00040443493600000814
Predicting the time before for the ith station position +.>
Figure BDA00040443493600000815
The history of the time passes into the wind, k represents the ordinal number of the time before the predicted time, +.>
Figure BDA00040443493600000816
Figure BDA00040443493600000817
Representing an upward rounding;
s320, calculating the comprehensive distance between the wind field prediction initial guess value and all the historical analysis grid point data
Figure BDA0004044349360000091
Figure BDA0004044349360000092
in the formula ,
Figure BDA0004044349360000093
the value of the nth variable, which represents the normalization of the ith station position and the jth-k predetermined time interval, where k represents the ordinal number of the time preceding the predicted time,/- >
Figure BDA0004044349360000094
Represents an upward rounding, n represents the ordinal number of the four variables, σ n Weights for the nth variable;
s330, calculating the comprehensive distance S between the initial vector of weft wind and the initial vector of warp wind and all historical analysis lattice point data ij ：
Figure BDA0004044349360000095
S340, calculating wind field prediction coefficients and utilizing ground altitude
Figure BDA0004044349360000096
Calculating a predicted estimated total number:
Figure BDA0004044349360000097
wherein
Figure BDA0004044349360000098
Representing an upward rounding;
by using the integrated distance S ij Selecting the minimum M distances to construct a nearest distance vector Smin im Where i denotes the i-th site position, M denotes the ordinal number of the nearest distance, m= … M; calculating wind field prediction coefficient omega ij ：
Figure BDA0004044349360000099
N=1 represents the variable ground 10 m wind direction, n=2 represents the variable 10 m wind speed, n=3 represents the variable 2 m temperature, and n=4 represents the variable ground air pressure;
the specific implementation method of the step 4 is as follows:
observing a data set Z using the historical analysis ij Is at a wind speed Z of 10 meters ij (wsp) and 10 m wind direction observations Z ij (wdir) calculating historically observed weft wind Zu ij And warp direction wind Zv ij The calculation formula is as follows:
Zu ij ＝-Z ij (wsp)×sin(Z ij (wdir))
Zv ij ＝-Z ij (wsp)×cos(Z ij (wdir))
calculating predicted weft wind Fu i And warp direction Fv i The calculation formula is as follows:
Figure BDA0004044349360000101
Figure BDA0004044349360000102
where i represents the ith site location, ω ij And predicting coefficients for the wind field.
The beneficial effects of the invention are as follows:
The wind field prediction method for the complex terrain of the power grid power transmission channel comprises the main factors of influence of the complex terrain, a near-ground wind field and a temperature field, and a wind field initial guess formula based on the altitude of the terrain is creatively provided by collecting wind field, temperature and air pressure data of the power grid power transmission channel in the past year.
Through establishing a data set, grid division is carried out on a complex terrain area of a power grid power transmission channel, region division with stronger practicability is achieved, meanwhile, time division is carried out at 0 time-24 time of each minute, each hour or each natural day, time division with stronger practicability is achieved, the established prediction equation depends on wind field, temperature and air pressure data of the power grid power transmission channel in the past year, powerful data support is provided for the prediction equation by utilizing grid point re-analysis data on the basis of defining main factors of complex terrain, near-ground wind field and temperature field, and accuracy of the prediction equation is greatly improved.
The influence of wind field, temperature and air pressure on wind field prediction is calculated through historical grid point data, weft wind and warp wind can be accurately calculated, the actually-occurring wind field, temperature field and air pressure field of the monitored area to be predicted are recorded in the data set, a prediction equation is optimized, and the accuracy of the prediction equation is improved.
Drawings
FIG. 1 is a block diagram of a system according to the present invention
The system comprises a 1-data collection processing module, a 2-prediction area data acquisition module, a 3-wind field prediction coefficient acquisition module and a 4-wind field prediction module.
Detailed Description
The invention is described in further detail below with reference to the attached drawings and specific examples:
a wind field prediction system of a complex terrain area of a power grid transmission channel is shown in fig. 1, and comprises a data collection processing module 1, a prediction area data acquisition module 2, a wind field prediction coefficient acquisition module 3 and a wind field prediction module 4;
the data collection processing module 1 is used for collecting historical observation data and mode grid point analysis data of a selected area of the power transmission channel, and obtaining historical data for prediction;
the predicted area data acquisition module 2 is used for acquiring site observation data variables of each automatic meteorological station in the selected area of the power transmission channel;
the wind field prediction coefficient acquisition module 3 acquires a wind field prediction coefficient based on historical observation data of the collection processing module 1 and site observation data variables of each automatic meteorological station in a power transmission channel selected area of the prediction area data acquisition module 2;
the wind field prediction module 4 predicts a wind field based on site observation data variables of each automatic meteorological station in the power transmission channel selected region of the prediction region data acquisition module 2 and the wind field prediction coefficient acquired by the wind field prediction coefficient acquisition module 3.
In the above technical solution, the specific implementation method of the data collection processing module 1 is as follows:
s110, dividing longitude and latitude grids of a selected area by a preset distance according to a power transmission channel, and dividing the natural year by a preset time interval;
s120, collecting historical observation data and mode grid point analysis data of the selected region of the power transmission channel for p years, wherein p is preferably 10 years in the embodiment;
s130, interpolating the mode grid point analysis data in each preset time interval to each automatic weather station site position in the historical observation data by using an interpolation algorithm; establishing a post-interpolation historical analysis observation data set and an post-interpolation grid point data set of each automatic weather station site position and each preset time interval delta t according to the post-interpolation mode grid point analysis data and the historical observation data; the historical analysis observation data set is Z ij The interpolated lattice point observation data is G ij, wherein ,Zij Site observations representing the ith site location, the jth predetermined time interval, G ij Interpolated grid point observation data representing an ith site location at a jth predetermined time interval;
s140, normalizing the grid point data, and re-analyzing the data set G for the interpolated grid point ij ' normalization processing is carried out to obtain a normalized data set
Figure BDA0004044349360000121
The formula of the normalization process is as follows:
Figure BDA0004044349360000122
according to the technical scheme, the historical observation data comprise the ground automatic weather station 10 m wind direction, the ground 10 m wind speed of the automatic weather station, the ground 2 m temperature of the automatic weather station and the ground air pressure of the automatic weather station, which are observed by the automatic weather station in each preset time interval on the power transmission channel;
the mode grid point analysis data comprise 10 m wind direction of the mode analysis ground, 10 m wind speed of the mode analysis ground, 2 m temperature of the mode analysis ground and the mode analysis ground air pressure in each longitude and latitude grid, wherein the minimum distance of the longitude and latitude grid is the horizontal resolution of the grid point analysis data.
In the above technical solution, the specific implementation manner of the interpolation algorithm is:
if the site position distance is greater than or equal to the horizontal resolution of the lattice point analysis data, selecting the interpolation algorithm as the nearest neighbor method; if the site position distance is smaller than the horizontal resolution of the lattice point re-analysis data, the interpolation algorithm is selected as a bilinear interpolation method, and the bilinear interpolation formula is as follows:
Figure BDA0004044349360000123
wherein Zij The position of the ith station is (x, y), and the adjacent four grid points are respectively analyzed to be data
Figure BDA0004044349360000131
Said->
Figure BDA0004044349360000132
Is (x) 1 ,y 1 ) Said->
Figure BDA0004044349360000133
Is (x) 2 ,y 1 ) Said->
Figure BDA0004044349360000134
Is (x) 1 ,y 2 ) Said->
Figure BDA0004044349360000135
Is (x) 2 ,y 2 )；
The interpolated lattice re-analysis data set G ij ' including interpolated 10 meters wind direction G ij (wdir) in degrees, interpolated 10 m wind speed G ij (spd) in m/s, interpolated 2 m temperature G ij (T) in K and interpolated ground air pressure G ij (P) in hPa.
In the above technical solution, the specific implementation method of the prediction area data acquisition module 2 is as follows:
acquiring site observation data variables of each automatic weather station in the selected area of the power transmission channel, wherein the site observation data variables of each automatic weather station in the selected area of the power transmission channel comprise weft wind
Figure BDA0004044349360000136
Wind in the channel->
Figure BDA0004044349360000137
And ground altitude->
Figure BDA0004044349360000138
Where i represents the ith site location, τ k And the historical time of the kth time before the predicted time acquired according to the preset time interval is represented, delta t represents the preset time interval, and the historical time of the kth time is the predicted time and is pushed forward for delta t multiplied by k hours.
In the above technical solution, the specific implementation method of the wind field prediction coefficient obtaining module 3 is as follows:
S310, calculating a wind field prediction initial guess value, and estimating a weft wind prediction initial guess value CFu by using site observation data variables of a selected area of a power transmission channel i (t) (in m/s) and a preliminary guess for warp wind prediction CFv i (t) (in m/s), where i represents the i-th site location and t represents the predicted time; the estimation formula is as follows:
Figure BDA0004044349360000139
Figure BDA00040443493600001310
/>
wherein ,
Figure BDA00040443493600001311
the historical moment weft wind of the 1 st time before the time is predicted for the ith site position,
Figure BDA00040443493600001312
predicting the historical time latitudinal wind of the (k+1) th time before the time for the (i) th site position,/for the (i) th site position>
Figure BDA00040443493600001313
Predicting the historical moment latitudinal wind of the kth time before the time for the ith site location,/>
Figure BDA00040443493600001314
Predicting the time before for the ith station position +.>
Figure BDA00040443493600001315
Weft wind at historical time of each time, < ->
Figure BDA0004044349360000141
The historical time of the 1 st time before the forecast time for the ith site location is windward,/>
Figure BDA0004044349360000142
Predicting the historical moment of the (k+1) th time before the time for the (i) th site position by wind,/wind>
Figure BDA0004044349360000143
Predicting the historical moment of the kth time before the time for the ith site location is windward,/>
Figure BDA0004044349360000144
Predicting the time before for the ith station position +.>
Figure BDA0004044349360000145
The history of the time passes into the wind, k represents the ordinal number of the time before the predicted time, +.>
Figure BDA0004044349360000146
Figure BDA0004044349360000147
Figure BDA0004044349360000148
In m, & gt>
Figure BDA0004044349360000149
Representing an upward rounding;
s320, calculating the comprehensive distance between the wind field prediction initial guess value and all the historical analysis grid point data
Calculating initial guess value of weft wind and the method
Figure BDA00040443493600001410
Distance Su at the ith site location ij Calculating initial guess of the wind direction and the +.>
Figure BDA00040443493600001411
Distance Sv of jth predetermined time interval ij The specific calculation formula is as follows:
Figure BDA00040443493600001412
Figure BDA00040443493600001413
in the formula ,
Figure BDA00040443493600001414
the value of the nth variable, which represents the normalization of the ith station position and the jth-k predetermined time interval, where k represents the ordinal number of the time preceding the predicted time,/->
Figure BDA00040443493600001415
Figure BDA00040443493600001416
In m, & gt>
Figure BDA00040443493600001417
Represents an upward rounding, n represents the ordinal number of the four variables, σ n As the weight of the nth variable, +.in this embodiment>
Figure BDA00040443493600001418
S330, calculating the comprehensive distance S between the initial vector of weft wind and the initial vector of warp wind and all historical analysis lattice point data ij ：
Figure BDA00040443493600001419
S340, calculating wind field prediction coefficients and utilizing ground altitude
Figure BDA00040443493600001420
Calculating a predicted estimated total number:
Figure BDA00040443493600001421
wherein
Figure BDA0004044349360000151
Representing an upward rounding;
by using the integrated distance S ij Selecting the minimum M distances to construct a nearest distance vector Smin im Where i denotes the i-th site position, M denotes the ordinal number of the nearest distance, m= … M; calculating wind field prediction coefficient omega ij ：
Figure BDA0004044349360000152
In the above technical solution, n=1 represents the variable ground 10 m wind direction, n=2 represents the variable ground 10 m wind speed, n=3 represents the variable ground 2 m temperature, and n=4 represents the variable ground air pressure.
In the above technical solution, the specific implementation method of the wind field prediction module 4 is as follows:
observing a data set Z using the historical analysis ij Is at a wind speed Z of 10 meters ij (wsp) and 10 m wind direction observations Z ij (wdir) calculating historically observed weft wind Zu ij And warp direction wind Zv ij The calculation formula is as follows:
Zu ij ＝-Z ij (wsp)×sin(Z ij (wdir))
Zv ij ＝-Z ij (wsp)×cos(Z ij (wdir))
calculating predicted weft wind Fu i And warp direction Fv i The calculation formula is as follows:
Figure BDA0004044349360000153
Figure BDA0004044349360000154
wherein i represents the ith site location，ω ij And predicting coefficients for the wind field.
A wind field prediction method for a complex terrain area of a power grid power transmission channel comprises the following steps:
step 1, collecting historical observation data and mode lattice point analysis data of a selected area of a power transmission channel, and obtaining historical data for prediction;
step 2, acquiring site observation data variables of each automatic meteorological station in a selected area of a power transmission channel;
step 3, wind field prediction coefficients are obtained based on the historical observation data and site observation data variables of each automatic meteorological station in the selected region of the power transmission channel;
and 4, predicting a wind field based on site observation data variables of each automatic meteorological station in the selected area of the power transmission channel and the wind field prediction coefficients.
The specific implementation method of the step 1 is as follows:
s110, dividing longitude and latitude grids of a selected area by a preset distance according to a power transmission channel, and dividing the natural year by a preset time interval;
S120, collecting historical observation data and mode grid point analysis data of the selected region of the power transmission channel for p years, wherein p is preferably 10 years in the embodiment;
s130, interpolating the mode grid point analysis data in each preset time interval to each automatic weather station site position in the historical observation data by using an interpolation algorithm; establishing a post-interpolation historical analysis observation data set and an post-interpolation grid point data set of each automatic weather station site position and each preset time interval delta t according to the post-interpolation mode grid point analysis data and the historical observation data; the historical analysis observation data set is Z ij The interpolated lattice point observation data is G ij, wherein ,Zij Site observations representing the ith site location, the jth predetermined time interval, G ij Interpolated grid point observation data representing an ith site location at a jth predetermined time interval;
s140, for interpolationLattice re-analysis dataset G ij ' normalization processing is carried out to obtain a normalized data set
Figure BDA0004044349360000161
The formula of the normalization process is as follows: />
Figure BDA0004044349360000162
The historical observation data comprises the ground automatic weather station 10 m wind direction, the ground automatic weather station 10 m wind speed, the ground automatic weather station 2 m temperature and the ground automatic weather station air pressure observed by the automatic weather station in each preset time interval on the power transmission channel;
The mode grid point analysis data comprise 10 m wind direction of a mode analysis ground, 10 m wind speed of the mode analysis ground, 2 m temperature of the mode analysis ground and mode analysis ground air pressure in each longitude and latitude grid, wherein the minimum distance of the longitude and latitude grid is the horizontal resolution of the grid point analysis data;
the specific implementation manner of the interpolation algorithm in the step S130 is as follows:
if the site position distance is greater than or equal to the horizontal resolution of the lattice point analysis data, selecting the interpolation algorithm as the nearest neighbor method; if the site position distance is smaller than the horizontal resolution of the lattice point re-analysis data, the interpolation algorithm is selected as a bilinear interpolation method, and the bilinear interpolation formula is as follows:
Figure BDA0004044349360000171
wherein Zij The position of the ith station is (x, y), and the adjacent four grid points are respectively analyzed to be data
Figure BDA0004044349360000172
Said->
Figure BDA0004044349360000173
Is (x) 1 ,y 1 ) Said->
Figure BDA0004044349360000174
Is (x) 2 ,y 1 ) Said->
Figure BDA0004044349360000175
Is (x) 1 ,y 2 ) Said->
Figure BDA0004044349360000176
Is (x) 2 ,y 2 )；
The interpolated lattice re-analysis data set G ij ' including interpolated 10 meters wind direction G ij (wdir), interpolated 10 m wind speed G ij (spd), interpolated 2 m temperature G ij (T) and the interpolated ground air pressure G ij (P)；
The specific implementation method of the step 2 is as follows:
acquiring site observation data variables of each automatic weather station in the selected area of the power transmission channel, wherein the site observation data variables of each automatic weather station in the selected area of the power transmission channel comprise weft wind
Figure BDA0004044349360000177
Wind in the channel->
Figure BDA0004044349360000178
And ground altitude->
Figure BDA0004044349360000179
Where i represents the ith site location, τ k And the historical time of the kth time before the predicted time acquired according to the preset time interval is represented, delta t represents the preset time interval, and the historical time of the kth time is the predicted time and is pushed forward for delta t multiplied by k hours.
The specific implementation method of the step 3 is as follows:
s310, estimating a weft wind prediction initial guess value CFu by using site observation data variables of selected areas of power transmission channels i (t) and preliminary guess for warp wind prediction CFv i (t), wherein i represents the i-th site location, and t represents the predicted time; the estimation formula is as follows:
Figure BDA0004044349360000181
Figure BDA0004044349360000182
wherein ,
Figure BDA0004044349360000183
the historical moment weft wind of the 1 st time before the time is predicted for the ith site position,
Figure BDA0004044349360000184
predicting the historical time latitudinal wind of the (k+1) th time before the time for the (i) th site position,/for the (i) th site position>
Figure BDA0004044349360000185
Predicting the historical moment latitudinal wind of the kth time before the time for the ith site location,/ >
Figure BDA0004044349360000186
Predicting the time before for the ith station position +.>
Figure BDA0004044349360000187
Weft wind at historical time of each time, < ->
Figure BDA0004044349360000188
The historical time of the 1 st time before the forecast time for the ith site location is windward,/>
Figure BDA0004044349360000189
Prediction for ith site locationThe history of the k+1th time before time is windy ++>
Figure BDA00040443493600001810
Predicting the historical moment of the kth time before the time for the ith site location is windward,/>
Figure BDA00040443493600001811
Predicting the time before for the ith station position +.>
Figure BDA00040443493600001812
The history of the time passes into the wind, k represents the ordinal number of the time before the predicted time, +.>
Figure BDA00040443493600001813
Figure BDA00040443493600001814
Representing an upward rounding;
s320, calculating the comprehensive distance between the wind field prediction initial guess value and all the historical analysis grid point data
Figure BDA00040443493600001815
in the formula ,
Figure BDA00040443493600001816
the value of the nth variable, which represents the normalization of the ith station position and the jth-k predetermined time interval, where k represents the ordinal number of the time preceding the predicted time,/->
Figure BDA00040443493600001817
Represents an upward rounding, n represents the ordinal number of the four variables, σ n Weights for the nth variable;
s330, calculating the comprehensive distance S between the initial vector of weft wind and the initial vector of warp wind and all historical analysis lattice point data ij ：
Figure BDA0004044349360000191
S340, calculating wind field prediction coefficients and utilizing ground altitude
Figure BDA0004044349360000192
Calculating a predicted estimated total number:
Figure BDA0004044349360000193
wherein
Figure BDA0004044349360000194
Representing an upward rounding; />
By using the integrated distance S ij Selecting the minimum M distances to construct a nearest distance vector Smin im Where i denotes the i-th site position, M denotes the ordinal number of the nearest distance, m= … M; calculating wind field prediction coefficient omega ij ：
Figure BDA0004044349360000195
N=1 represents the variable ground 10 m wind direction, n=2 represents the variable 10 m wind speed, n=3 represents the variable 2 m temperature, and n=4 represents the variable ground air pressure;
the specific implementation method of the step 4 is as follows:
observing a data set Z using the historical analysis ij Is at a wind speed Z of 10 meters ij (wsp) and 10 m wind direction observations Z ij (wdir) calculating historically observed weft wind Zu ij And warp direction wind Zv ij The calculation formula is as follows:
Zu ij ＝-Z ij (wsp)×sin(Z ij (wdir))
Zv ij ＝-Z ij (wsp)×cos(Z ij (wdir))
calculating predicted weft wind Fu i And warp direction Fv i The calculation formula is as follows:
Figure BDA0004044349360000196
Figure BDA0004044349360000197
where i represents the ith site location, ω ij And predicting coefficients for the wind field.
What is not described in detail in this specification is prior art known to those skilled in the art. It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention 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 invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
It should be finally understood that the foregoing embodiments are merely illustrative of the technical solutions of the present invention and not limiting the scope of protection thereof, and although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those skilled in the art that various changes, modifications or equivalents may be made to the specific embodiments of the invention, and these changes, modifications or equivalents are within the scope of protection of the claims appended hereto.

Claims (10)

1. A wind field prediction system of a complex terrain area of a power grid power transmission channel is characterized in that: the wind field prediction system comprises a data collection processing module (1), a prediction area data acquisition module (2), a wind field prediction coefficient acquisition module (3) and a wind field prediction module (4);
The data collection processing module (1) is used for collecting historical observation data and mode grid point analysis data of a selected area of the power transmission channel, and obtaining historical data for prediction;
the prediction area data acquisition module (2) is used for acquiring site observation data variables of each automatic meteorological station in the selected area of the power transmission channel;
the wind field prediction coefficient acquisition module (3) acquires a wind field prediction coefficient based on historical observation data of the collection processing module (1) and site observation data variables of each automatic meteorological station in a power transmission channel selected area of the prediction area data acquisition module (2);
the wind field prediction module (4) predicts a wind field based on site observation data variables of each automatic meteorological station in the power transmission channel selected area of the prediction area data acquisition module (2) and the wind field prediction coefficients acquired by the wind field prediction coefficient acquisition module (3).
2. A wind field prediction system based on a complex terrain area of a power transmission channel of a power grid as claimed in claim 1, wherein: the specific implementation method of the data collection processing module (1) comprises the following steps:
s110, dividing longitude and latitude grids of a selected area by a preset distance according to a power transmission channel, and dividing the natural year by a preset time interval;
S120, collecting historical observation data and mode grid point analysis data of the selected region of the power transmission channel for p years, wherein p is preferably 10 years in the embodiment;
s130, interpolating the mode grid point analysis data in each preset time interval to each automatic weather station site position in the historical observation data by using an interpolation algorithm; establishing a post-interpolation historical analysis observation data set and an post-interpolation grid point data set of each automatic weather station site position and each preset time interval delta t according to the post-interpolation mode grid point analysis data and the historical observation data; the historical analysis observation data set is Z ij The interpolated lattice point observation data is G ij, wherein ,Zij Site observations representing the ith site location, the jth predetermined time interval, G ij Interpolated grid point observation data representing an ith site location at a jth predetermined time interval;
s140, re-analyzing the data set G for the interpolated lattice ij ' normalization processing is carried out to obtain a normalized data set
Figure FDA0004044349350000021
The formula of the normalization process is as follows:
Figure FDA0004044349350000022
3. a wind field prediction system based on a complex terrain area of a power transmission channel of a power grid as claimed in claim 2, wherein:
The historical observation data comprises the ground automatic weather station 10 m wind direction, the ground automatic weather station 10 m wind speed, the ground automatic weather station 2 m temperature and the ground automatic weather station air pressure observed by the automatic weather station in each preset time interval on the power transmission channel;
the mode grid point analysis data comprise 10 m wind direction of the mode analysis ground, 10 m wind speed of the mode analysis ground, 2 m temperature of the mode analysis ground and the mode analysis ground air pressure in each longitude and latitude grid, wherein the minimum distance of the longitude and latitude grid is the horizontal resolution of the grid point analysis data.
4. A wind field prediction system based on a complex terrain area of a power transmission channel of a power grid as claimed in claim 2, wherein: the specific implementation mode of the interpolation algorithm is as follows:
if the site position distance is greater than or equal to the horizontal resolution of the lattice point analysis data, selecting the interpolation algorithm as the nearest neighbor method; if the site position distance is smaller than the horizontal resolution of the lattice point re-analysis data, the interpolation algorithm is selected as a bilinear interpolation method, and the bilinear interpolation formula is as follows:
Figure FDA0004044349350000023
wherein Zij The position of the ith station is (x, y), and the adjacent four grid points are respectively analyzed to be data
Figure FDA0004044349350000024
Said->
Figure FDA0004044349350000025
Is (x) 1 ,y 1 ) Said->
Figure FDA0004044349350000031
Is (x) 2 ,y 1 ) Said->
Figure FDA0004044349350000032
Is (x) 1 ,y 2 ) Said->
Figure FDA0004044349350000033
Is (x) 2 ,y 2 )；
The interpolated lattice re-analysis data set G ij ' including interpolated 10 meters wind direction G ij (wdir), interpolated 10 m wind speed G ij (spd), interpolated 2 m temperature G ij (T) and the interpolated ground air pressure G ij (P)。
5. A wind field prediction system based on a complex terrain area of a power transmission channel of a power grid as claimed in claim 1, wherein: the specific implementation method of the prediction area data acquisition module (2) comprises the following steps:
acquiring site observation data variables of each automatic weather station in the selected area of the power transmission channel, wherein the site observation data variables of each automatic weather station in the selected area of the power transmission channel comprise weft wind
Figure FDA0004044349350000034
Wind in the channel->
Figure FDA0004044349350000035
And ground altitude->
Figure FDA0004044349350000036
Where i represents the ith site location, τ k And the historical time of the kth time before the predicted time acquired according to the preset time interval is represented, delta t represents the preset time interval, and the historical time of the kth time is the predicted time and is pushed forward for delta t multiplied by k hours.
6. A wind field prediction system based on a complex terrain area of a power transmission channel of a power grid as claimed in claim 1, wherein: the specific implementation method of the wind field prediction coefficient acquisition module (3) comprises the following steps:
S310, estimating a weft wind prediction initial guess value CFu by using site observation data variables of selected areas of power transmission channels i (t) and preliminary guess for warp wind prediction CFv i (t), wherein i represents the i-th site location, and t represents the predicted time; the estimation formula is as follows:
Figure FDA0004044349350000037
Figure FDA0004044349350000038
wherein ,
Figure FDA0004044349350000039
predicting the historical moment latitudinal wind of the 1 st time before the time for the ith site location,/>
Figure FDA00040443493500000310
Predicting the historical time latitudinal wind of the (k+1) th time before the time for the (i) th site position,/for the (i) th site position>
Figure FDA00040443493500000311
Predicting the historical moment latitudinal wind of the kth time before the time for the ith site location,/>
Figure FDA00040443493500000312
For the ith site locationPredicted time before->
Figure FDA00040443493500000313
Weft wind at historical time of each time, < ->
Figure FDA0004044349350000041
The historical moment of the 1 st time before the predicted time is windward for the ith site location,
Figure FDA0004044349350000042
predicting the historical moment of the (k+1) th time before the time for the (i) th site position by wind,/wind>
Figure FDA0004044349350000043
Predicting the historical moment of the kth time before the time for the ith site location is windward,/>
Figure FDA0004044349350000044
Predicting the time before for the ith station position +.>
Figure FDA00040443493500000415
The history of the time passes into the wind, k represents the ordinal number of the time before the predicted time, +.>
Figure FDA0004044349350000045
Figure FDA0004044349350000046
Representing an upward rounding;
s320, calculating the comprehensive distance between the wind field prediction initial guess value and all the historical analysis grid point data
Figure FDA0004044349350000047
Figure FDA0004044349350000048
in the formula ,
Figure FDA0004044349350000049
The value of the nth variable, which represents the normalization of the ith station position and the jth-k predetermined time interval, where k represents the ordinal number of the time preceding the predicted time,/->
Figure FDA00040443493500000410
Represents an upward rounding, n represents the ordinal number of the four variables, σ n Weights for the nth variable;
s330, calculating the comprehensive distance S between the initial vector of weft wind and the initial vector of warp wind and all historical analysis lattice point data ij ：
Figure FDA00040443493500000411
S340, calculating wind field prediction coefficients and utilizing ground altitude
Figure FDA00040443493500000412
Calculating a predicted estimated total number:
Figure FDA00040443493500000413
wherein
Figure FDA00040443493500000414
Representing an upward rounding;
by using the integrated distance S ij Selecting the minimum M distances to construct a nearest distance vector Smin im Where i denotes the i-th site position, M denotes the ordinal number of the nearest distance, m= … M; calculating wind field prediction coefficient omega ij ：
Figure FDA0004044349350000051
7. A wind field prediction system for a complex terrain area of a power grid transmission channel based on claim 6, wherein:
the n=1 represents the variable ground 10 m wind direction, n=2 represents the variable 10 m wind speed, n=3 represents the variable 2 m temperature, and n=4 represents the variable ground air pressure.
8. A wind field prediction system based on a complex terrain area of a power transmission channel of a power grid as claimed in claim 1, wherein: the specific implementation method of the wind field prediction module (4) comprises the following steps:
Observing a data set Z using the historical analysis ij Is at a wind speed Z of 10 meters ij (wsp) and 10 m wind direction observations Z ij (wdir) calculating historically observed weft wind Zu ij And warp direction wind Zv ij The calculation formula is as follows:
Zu ij ＝-Z ij (wsp)×sin(Z ij (wdir))
Zv ij ＝-Z ij (wsp)×cos(Z ij (wdir))
calculating predicted weft wind Fu i And warp direction Fv i The calculation formula is as follows:
Figure FDA0004044349350000052
Figure FDA0004044349350000053
where i represents the ith site location, ω ij And predicting coefficients for the wind field.
9. A wind field prediction method for a complex terrain area of a power transmission channel of a power grid is characterized by comprising the following steps of: it comprises the following steps:
step 1, collecting historical observation data and mode lattice point analysis data of a selected area of a power transmission channel, and obtaining historical data for prediction;
step 2, acquiring site observation data variables of each automatic meteorological station in a selected area of a power transmission channel;
step 3, based on the historical observation data and site observation data variables of each automatic meteorological station in the selected area of the power transmission channel;
and 4, predicting a wind field based on site observation data variables of each automatic meteorological station in the selected area of the power transmission channel and the wind field prediction coefficients.
10. The wind field prediction method based on the complex terrain area of the power transmission channel of the power grid is characterized by comprising the following steps of:
the specific implementation method of the step 1 is as follows:
S110, dividing longitude and latitude grids of a selected area by a preset distance according to a power transmission channel, and dividing the natural year by a preset time interval;
s120, collecting historical observation data and mode grid point analysis data of the selected region of the power transmission channel for p years, wherein p is preferably 10 years in the embodiment;
s130, interpolating the mode grid point analysis data in each preset time interval to each automatic weather station site position in the historical observation data by using an interpolation algorithm; establishing a post-interpolation historical analysis observation data set and an post-interpolation grid point data set of each automatic weather station site position and each preset time interval delta t according to the post-interpolation mode grid point analysis data and the historical observation data; the historical analysis observation data set is Z ij The interpolated lattice point observation data is G ij, wherein ,Zij Site observations representing the ith site location, the jth predetermined time interval, G ij Interpolated grid point observation data representing an ith site location at a jth predetermined time interval;
s140, re-analyzing the data set G for the interpolated lattice ij ' normalization processing is carried out to obtain a normalized data set
Figure FDA0004044349350000061
The formula of the normalization process is as follows:
Figure FDA0004044349350000062
the historical observation data comprises the ground automatic weather station 10 m wind direction, the ground automatic weather station 10 m wind speed, the ground automatic weather station 2 m temperature and the ground automatic weather station air pressure observed by the automatic weather station in each preset time interval on the power transmission channel;
the mode grid point analysis data comprise 10 m wind direction of a mode analysis ground, 10 m wind speed of the mode analysis ground, 2 m temperature of the mode analysis ground and mode analysis ground air pressure in each longitude and latitude grid, wherein the minimum distance of the longitude and latitude grid is the horizontal resolution of the grid point analysis data;
the specific implementation manner of the interpolation algorithm in the step S130 is as follows:
if the site position distance is greater than or equal to the horizontal resolution of the lattice point analysis data, selecting the interpolation algorithm as the nearest neighbor method; if the site position distance is smaller than the horizontal resolution of the lattice point re-analysis data, the interpolation algorithm is selected as a bilinear interpolation method, and the bilinear interpolation formula is as follows:
Figure FDA0004044349350000071
wherein Zij The position of the ith station is (x, y), and the adjacent four grid points are respectively analyzed to be data
Figure FDA0004044349350000072
Said->
Figure FDA0004044349350000073
Is (x) 1 ,y 1 ) Said->
Figure FDA0004044349350000074
Is (x) 2 ,y 1 ) Said->
Figure FDA0004044349350000075
Is (x) 1 ,y 2 ) Said->
Figure FDA0004044349350000076
Is (x) 2 ,y 2 )；
The interpolated lattice re-analysis data set G ij ' including interpolated 10 meters wind direction G ij (wdir), interpolated 10 m wind speed G ij (spd), interpolated 2 m temperature G ij (T) and the interpolated ground air pressure G ij (P)；
The specific implementation method of the step 2 is as follows:
acquiring site observation data variables of each automatic weather station in the selected area of the power transmission channel, wherein the site observation data variables of each automatic weather station in the selected area of the power transmission channel comprise weft wind
Figure FDA0004044349350000077
Wind in the channel->
Figure FDA0004044349350000078
And ground altitude->
Figure FDA0004044349350000079
Where i represents the ith site location, τ k Represents the historical moment of the kth time before the predicted time obtained according to the predetermined time interval, Δt represents the predetermined time interval,the historical time of the kth time is the predicted time and is pushed forward by delta t multiplied by k hours;
the specific implementation method of the step 3 is as follows:
s310, estimating a weft wind prediction initial guess value CFu by using site observation data variables of selected areas of power transmission channels i (t) and preliminary guess for warp wind prediction CFv i (t), wherein i represents the i-th site location, and t represents the predicted time; the estimation formula is as follows:
Figure FDA0004044349350000081
/>
Figure FDA0004044349350000082
wherein ,
Figure FDA0004044349350000083
predicting the historical moment latitudinal wind of the 1 st time before the time for the ith site location,/>
Figure FDA0004044349350000084
Predicting the historical time latitudinal wind of the (k+1) th time before the time for the (i) th site position,/for the (i) th site position>
Figure FDA0004044349350000085
Predicting the historical moment latitudinal wind of the kth time before the time for the ith site location,/>
Figure FDA0004044349350000086
Predicting the time before for the ith station position +.>
Figure FDA0004044349350000087
Weft wind at historical time of each time, < ->
Figure FDA0004044349350000088
The historical moment of the 1 st time before the predicted time is windward for the ith site location,
Figure FDA0004044349350000089
predicting the historical moment of the (k+1) th time before the time for the (i) th site position by wind,/wind>
Figure FDA00040443493500000810
Predicting the historical moment of the kth time before the time for the ith site location is windward,/>
Figure FDA00040443493500000811
Predicting the time before for the ith station position +.>
Figure FDA00040443493500000812
The history of the time passes into the wind, k represents the ordinal number of the time before the predicted time, +.>
Figure FDA00040443493500000813
Figure FDA00040443493500000814
Representing an upward rounding;
s320, calculating the comprehensive distance between the wind field prediction initial guess value and all the historical analysis grid point data
Figure FDA00040443493500000815
Figure FDA00040443493500000816
in the formula ,
Figure FDA00040443493500000817
representing the ith site locationSetting the value of the nth variable normalized to the j-kth predetermined time interval, wherein k represents the ordinal number of the time preceding the predicted time, +.>
Figure FDA00040443493500000818
Represents an upward rounding, n represents the ordinal number of the four variables, σ n Weights for the nth variable;
S330, calculating the comprehensive distance S between the initial vector of weft wind and the initial vector of warp wind and all historical analysis lattice point data ij ：
Figure FDA0004044349350000091
S340, calculating wind field prediction coefficients and utilizing ground altitude
Figure FDA0004044349350000092
Calculating a predicted estimated total number:
Figure FDA0004044349350000093
wherein
Figure FDA0004044349350000094
Representing an upward rounding;
by using the integrated distance S ij Selecting the minimum M distances to construct a nearest distance vector Smin im Where i denotes the i-th site position, M denotes the ordinal number of the nearest distance, m= … M; calculating wind field prediction coefficient omega ij ：
Figure FDA0004044349350000095
/>
N=1 represents the variable ground 10 m wind direction, n=2 represents the variable 10 m wind speed, n=3 represents the variable 2 m temperature, and n=4 represents the variable ground air pressure;
the specific implementation method of the step 4 is as follows:
observing a data set Z using the historical analysis ij Is at a wind speed Z of 10 meters ij (wsp) and 10 m wind direction observations Z ij (wdir) calculating historically observed weft wind Zu ij And warp direction wind Zv ij The calculation formula is as follows:
Zu ij ＝-Z ij (wsp)×sin(Z ij (wdir))
Zv ij ＝-Z ij (wsp)×cos(Z ij (wdir))
calculating predicted weft wind Fu i And warp direction Fv i The calculation formula is as follows:
Figure FDA0004044349350000096
Figure FDA0004044349350000097
where i represents the ith site location, ω ij And predicting coefficients for the wind field.
CN202310025361.0A 2023-01-09 2023-01-09 Wind field prediction system and method for complex terrain area of power grid power transmission channel Pending CN116167211A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118153401A (en) * 2024-05-09 2024-06-07 深圳智荟物联技术有限公司 Wind field prediction method, device, equipment and storage medium

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118153401A (en) * 2024-05-09 2024-06-07 深圳智荟物联技术有限公司 Wind field prediction method, device, equipment and storage medium

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