CN116167211B - A wind field prediction system and method for complex terrain areas of power grid transmission channels - Google Patents
A wind field prediction system and method for complex terrain areas of power grid transmission channels Download PDFInfo
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Abstract
本发明公开了电网输电通道复杂地形区域的风场预测系统及方法，收集输电通道选定区域的历史观测数据和模式格点再分析数据，获取用于预测的历史数据；获取预测区域的站点观测数据变量；基于历史观测数据和站点观测数据变量获取风场预测系数；基于站点观测数据变量和风场预测系数预测风场。本发明的电网输电通道复杂地形风场预测方法，包含了复杂地形、近地面风场和温度场为影响的主要因素。本发明建立数据集实现了实用性更强的地域划分和时间划分，建立的预测方程依托电网输电通道历年的风场、温度和气压数据，在明确复杂地形、近地面风场和温度场主要因素的基础上，利用格点再分析数据为预测方程提供了有力的数据支撑，预测方程准确度大幅提高。
The present invention discloses a wind field prediction system and method for complex terrain areas of power grid transmission channels, which collects historical observation data and pattern grid reanalysis data of selected areas of the transmission channel to obtain historical data for prediction; obtains site observation data variables of the prediction area; obtains wind field prediction coefficients based on historical observation data and site observation data variables; and predicts the wind field based on site observation data variables and wind field prediction coefficients. The wind field prediction method for complex terrain of power grid transmission channels of the present invention includes complex terrain, near-ground wind field and temperature field as the main influencing factors. The present invention establishes a data set to achieve more practical regional division and time division. The established prediction equation relies on the wind field, temperature and air pressure data of the power grid transmission channel over the years. On the basis of clarifying the main factors of complex terrain, near-ground wind field and temperature field, the grid reanalysis data is used to provide strong data support for the prediction equation, and the accuracy of the prediction equation is greatly improved.
Description
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, a numerical solution model is established directly according to the evolution of similar historical examples as the basis of current prediction or directly 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 power grid transmission channel complex terrain area 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, wherein the data collection processing module is used for collecting historical observation data and mode grid point analysis data of a transmission channel selected area to acquire 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 transmission channel selected area, the wind field prediction coefficient acquisition module is used for acquiring wind field prediction coefficients based on the historical observation data of the collection processing module and the site observation data variables of each automatic meteorological station in the transmission channel selected area, and the wind field prediction module is used for predicting wind fields based on the site observation data variables of each automatic meteorological station in the transmission channel selected area 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;
The method comprises the steps of S130, using an interpolation algorithm to interpolate the mode lattice point analysis data in each preset time interval to each automatic weather station site position in the historical observation data, and establishing an interpolated historical analysis observation data set and an interpolated lattice point data set of each automatic weather station site position, each preset time interval delta t according to the interpolated mode lattice point analysis data and the historical observation data, wherein the historical analysis observation data set is Z ij, the interpolated lattice point observation data is G ij, Z ij represents a site observation value of an ith site position in a jth preset time interval, and G ij represents interpolated lattice point observation data of the ith site position in the jth preset time interval;
S140, carrying out normalization processing on the interpolated lattice point re-analysis dataset G ij' to obtain a normalized dataset The formula of the normalization process is as follows:
The historical observation data comprises the ground automatic weather station 10m wind direction, the ground automatic weather station 10m wind speed, the ground automatic weather station 2m 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 10m wind direction of the mode analysis ground, 10m wind speed of the mode analysis ground, 2m 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 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:
wherein the position of the ith station where Z ij is located is (x, y), and the adjacent four grid points thereof are respectively used for analyzing data The saidIs (x 1,y1), saidIs (x 2,y1), saidIs (x 1,y2), saidPosition (x 2,y2);
The interpolated lattice re-analysis dataset G ij' includes interpolated 10 meter wind direction G ij (wdir), interpolated 10 meter wind speed G ij (spd), interpolated 2 meter temperature G ij (T) and 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 Wind in the warp directionAnd ground altitudeWherein i represents the i-th site position, τ k represents the historical time of the kth time before the predicted time obtained according to the predetermined time interval, Δt represents the predetermined time interval, and the historical time of the kth time is the predicted time and is pushed forward by Δt×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 i (t) and a warp wind prediction initial guess value CFv i (t) by using site observation data variables of a selected area of a power transmission channel, wherein i represents an ith site position and t represents a prediction time, and the estimation formula is as follows:
wherein, The historical moment weft wind of the 1 st time before the time is predicted for the ith site position,The historical moment weft wind for the (k+1) th time before the time is predicted for the (i) th site location,Predicting a historical moment weft wind of the kth time before the time for the ith site position,Predicting the time-before-time for the ith site locationThe wind is weft-wise at the historical moment of each time,The historical moment of the 1 st time before the predicted time is windward for the ith site location,The historical moment of the kth +1 time before the time is predicted for the ith site location is windward,The historical moment of the kth time before the time is predicted for the ith site location is windward,Predicting the time-before-time for the ith site locationThe historical time of each time passes into the wind, k represents the ordinal number of the time before the predicted time, 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
In the formula,Representing the value of the nth variable normalized by the ith station location and the jth-k predetermined time interval, where k represents the ordinal number of the time preceding the predicted time,Represents an upward rounding, n represents ordinal numbers of the four variables, and sigma n is the weight of the nth variable;
S330, calculating the comprehensive distance S ij between the initial weft wind guess vector and all historical analysis grid point data:
S340, calculating wind field prediction coefficients and utilizing ground altitude Calculating a predicted estimated total number:
Wherein the method comprises the steps of Representing an upward rounding;
Selecting the minimum M distances by utilizing the comprehensive distance S ij, constructing a nearest distance vector Smin im, wherein i represents the position of the ith station, M represents the ordinal number of the nearest distance, m=1..M, and calculating a wind field prediction coefficient omega ij:
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 2m temperature, and n=4 represents the variable ground air pressure.
The specific implementation method of the wind field prediction module 4 is as follows:
Using the historical analysis observation dataset Z ij, a 10 meter wind speed Z ij (wsp) and a 10 meter wind direction observation value Z ij (wdir), a historical observed weft wind Zu ij and warp wind Zv ij are calculated according to the following calculation formula:
Zuij＝-Zij(wsp)×sin(Zij(wdir))
Zvij＝-Zij(wsp)×cos(Zij(wdir))
the predicted weft wind Fu i and warp wind Fv i are calculated as:
Wherein i represents the ith station position, and ω ij is the wind field prediction coefficient.
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;
The method comprises the steps of S130, using an interpolation algorithm to interpolate the mode lattice point analysis data in each preset time interval to each automatic weather station site position in the historical observation data, and establishing an interpolated historical analysis observation data set and an interpolated lattice point data set of each automatic weather station site position, each preset time interval delta t according to the interpolated mode lattice point analysis data and the historical observation data, wherein the historical analysis observation data set is Z ij, the interpolated lattice point observation data is G ij, Z ij represents a site observation value of an ith site position in a jth preset time interval, and G ij represents interpolated lattice point observation data of the ith site position in the jth preset time interval;
S140, carrying out normalization processing on the interpolated lattice point re-analysis dataset G ij' to obtain a normalized dataset The formula of the normalization process is as follows:
The historical observation data comprises the ground automatic weather station 10m wind direction, the ground automatic weather station 10m wind speed, the ground automatic weather station 2m 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 10m wind direction of a mode analysis ground, 10m wind speed of the mode analysis ground, 2m 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 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:
wherein the position of the ith station where Z ij is located is (x, y), and the adjacent four grid points thereof are respectively used for analyzing data The saidIs (x 1,y1), saidIs (x 2,y1), saidIs (x 1,y2), saidPosition (x 2,y2);
The interpolated lattice point re-analysis dataset G ij' comprises an interpolated 10 meter wind direction G ij (wdir), an interpolated 10 meter wind speed G ij (spd), an interpolated 2 meter temperature G ij (T) and an 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 Wind in the warp directionAnd ground altitudeWherein i represents the i-th site position, τ k represents the historical time of the kth time before the predicted time obtained according to the predetermined time interval, Δt represents the predetermined time interval, and the historical time of the kth time is the predicted time and is pushed forward by Δt×k hours.
The specific implementation method of the step 3 is as follows:
S310, estimating a weft wind prediction initial guess value CFu i (t) and a warp wind prediction initial guess value CFv i (t) by using site observation data variables of a selected area of a power transmission channel, wherein i represents an ith site position and t represents a prediction time, and the estimation formula is as follows:
wherein, The historical moment weft wind of the 1 st time before the time is predicted for the ith site position,The historical moment weft wind for the (k+1) th time before the time is predicted for the (i) th site location,Predicting a historical moment weft wind of the kth time before the time for the ith site position,Predicting the time-before-time for the ith site locationThe wind is weft-wise at the historical moment of each time,The historical moment of the 1 st time before the predicted time is windward for the ith site location,The historical moment of the kth +1 time before the time is predicted for the ith site location is windward,The historical moment of the kth time before the time is predicted for the ith site location is windward,Predicting the time-before-time for the ith site locationThe historical time of each time passes into the wind, k represents the ordinal number of the time before the predicted time, 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
In the formula,Representing the value of the nth variable normalized by the ith station location and the jth-k predetermined time interval, where k represents the ordinal number of the time preceding the predicted time,Represents an upward rounding, n represents ordinal numbers of the four variables, and sigma n is the weight of the nth variable;
S330, calculating the comprehensive distance S ij between the initial weft wind guess vector and all historical analysis grid point data:
S340, calculating wind field prediction coefficients and utilizing ground altitude Calculating a predicted estimated total number:
Wherein the method comprises the steps of Representing an upward rounding;
Selecting the minimum M distances by utilizing the comprehensive distance S ij, constructing a nearest distance vector Smin im, wherein i represents the position of the ith station, M represents the ordinal number of the nearest distance, m=1..M, and calculating a wind field prediction coefficient omega ij:
n=1 represents the variable ground 10m wind direction, n=2 represents the variable 10m 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:
Using the historical analysis observation dataset Z ij, a 10 meter wind speed Z ij (wsp) and a 10 meter wind direction observation value Z ij (wdir), a historical observed weft wind Zu ij and warp wind Zv ij are calculated according to the following calculation formula:
Zuij＝-Zij(wsp)×sin(Zij(wdir))
Zvij＝-Zij(wsp)×cos(Zij(wdir))
the predicted weft wind Fu i and warp wind Fv i are calculated as:
Wherein i represents the ith station position, and ω ij is the wind field prediction coefficient.
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;
The method comprises the steps of S130, using an interpolation algorithm to interpolate the mode lattice point analysis data in each preset time interval to each automatic weather station site position in the historical observation data, and establishing an interpolated historical analysis observation data set and an interpolated lattice point data set of each automatic weather station site position, each preset time interval delta t according to the interpolated mode lattice point analysis data and the historical observation data, wherein the historical analysis observation data set is Z ij, the interpolated lattice point observation data is G ij, Z ij represents a site observation value of an ith site position in a jth preset time interval, and G ij represents interpolated lattice point observation data of the ith site position in the jth preset time interval;
s140, normalizing the grid point data, and carrying out normalization processing on the interpolated grid point re-analysis data set G ij' to obtain a normalized data set The formula of the normalization process is as follows:
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 2m 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 10m wind direction of the mode analysis ground, 10m wind speed of the mode analysis ground, 2m 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 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:
wherein the position of the ith station where Z ij is located is (x, y), and the adjacent four grid points thereof are respectively used for analyzing data The saidIs (x 1,y1), saidIs (x 2,y1), saidIs (x 1,y2), saidPosition (x 2,y2);
The interpolated lattice re-analysis dataset G ij' includes interpolated 10 meters wind direction G ij (wdir), in degrees, interpolated 10 meters wind speed G ij (spd), in m/s, interpolated 2 meters 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 Wind in the warp directionAnd ground altitudeWherein i represents the i-th site position, τ k represents the historical time of the kth time before the predicted time obtained according to the predetermined time interval, Δt represents the predetermined time interval, and the historical time of the kth time is the predicted time and is pushed forward by Δt×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 i (t) (unit is m/S) and a warp wind prediction initial guess value CFv i (t) (unit is m/S) by using site observation data variables of a selected area of a power transmission channel, wherein i represents an ith site position and t represents prediction time, and the estimation formula is as follows:
wherein, The historical moment weft wind of the 1 st time before the time is predicted for the ith site position,The historical moment weft wind for the (k+1) th time before the time is predicted for the (i) th site location,Predicting a historical moment weft wind of the kth time before the time for the ith site position,Predicting the time-before-time for the ith site locationThe wind is weft-wise at the historical moment of each time,The historical moment of the 1 st time before the predicted time is windward for the ith site location,The historical moment of the kth +1 time before the time is predicted for the ith site location is windward,The historical moment of the kth time before the time is predicted for the ith site location is windward,Predicting the time-before-time for the ith site locationThe historical time of each time passes into the wind, k represents the ordinal number of the time before the predicted time, The unit of (c) is m,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 methodCalculating initial guess of the distance Su ij at the ith site location and the initial guess of the wind directionThe j-th preset time interval distance Sv ij has the following specific calculation formula:
In the formula, Representing the value of the nth variable normalized by the ith station location and the jth-k predetermined time interval, where k represents the ordinal number of the time preceding the predicted time, The unit of (c) is m,Represents the rounding up, n represents the ordinal number of the four variables, σ n is the weight of the nth variable, in this embodiment
S330, calculating the comprehensive distance S ij between the initial weft wind guess vector and all historical analysis grid point data:
S340, calculating wind field prediction coefficients and utilizing ground altitude Calculating a predicted estimated total number:
Wherein the method comprises the steps of Representing an upward rounding;
Selecting the minimum M distances by utilizing the comprehensive distance S ij, constructing a nearest distance vector Smin im, wherein i represents the position of the ith station, M represents the ordinal number of the nearest distance, m=1..M, and calculating a wind field prediction coefficient omega ij:
In the above technical solution, n=1 represents the variable ground 10m wind direction, n=2 represents the variable ground 10m wind speed, n=3 represents the variable ground 2m 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:
Using the historical analysis observation dataset Z ij, a 10 meter wind speed Z ij (wsp) and a 10 meter wind direction observation value Z ij (wdir), a historical observed weft wind Zu ij and warp wind Zv ij are calculated according to the following calculation formula:
Zuij＝-Zij(wsp)×sin(Zij(wdir))
Zvij＝-Zij(wsp)×cos(Zij(wdir))
the predicted weft wind Fu i and warp wind Fv i are calculated as:
Wherein i represents the ith station position, and ω ij is the wind field prediction coefficient.
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;
The method comprises the steps of S130, using an interpolation algorithm to interpolate the mode lattice point analysis data in each preset time interval to each automatic weather station site position in the historical observation data, and establishing an interpolated historical analysis observation data set and an interpolated lattice point data set of each automatic weather station site position, each preset time interval delta t according to the interpolated mode lattice point analysis data and the historical observation data, wherein the historical analysis observation data set is Z ij, the interpolated lattice point observation data is G ij, Z ij represents a site observation value of an ith site position in a jth preset time interval, and G ij represents interpolated lattice point observation data of the ith site position in the jth preset time interval;
S140, carrying out normalization processing on the interpolated lattice point re-analysis dataset G ij' to obtain a normalized dataset The formula of the normalization process is as follows:
The historical observation data comprises the ground automatic weather station 10m wind direction, the ground automatic weather station 10m wind speed, the ground automatic weather station 2m 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 10m wind direction of a mode analysis ground, 10m wind speed of the mode analysis ground, 2m 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 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:
wherein the position of the ith station where Z ij is located is (x, y), and the adjacent four grid points thereof are respectively used for analyzing data The saidIs (x 1,y1), saidIs (x 2,y1), saidIs (x 1,y2), saidPosition (x 2,y2);
The interpolated lattice point re-analysis dataset G ij' comprises an interpolated 10 meter wind direction G ij (wdir), an interpolated 10 meter wind speed G ij (spd), an interpolated 2 meter temperature G ij (T) and an 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 Wind in the warp directionAnd ground altitudeWherein i represents the i-th site position, τ k represents the historical time of the kth time before the predicted time obtained according to the predetermined time interval, Δt represents the predetermined time interval, and the historical time of the kth time is the predicted time and is pushed forward by Δt×k hours.
The specific implementation method of the step 3 is as follows:
S310, estimating a weft wind prediction initial guess value CFu i (t) and a warp wind prediction initial guess value CFv i (t) by using site observation data variables of a selected area of a power transmission channel, wherein i represents an ith site position and t represents a prediction time, and the estimation formula is as follows:
wherein, The historical moment weft wind of the 1 st time before the time is predicted for the ith site position,The historical moment weft wind for the (k+1) th time before the time is predicted for the (i) th site location,Predicting a historical moment weft wind of the kth time before the time for the ith site position,Predicting the time-before-time for the ith site locationThe wind is weft-wise at the historical moment of each time,The historical moment of the 1 st time before the predicted time is windward for the ith site location,The historical moment of the kth +1 time before the time is predicted for the ith site location is windward,The historical moment of the kth time before the time is predicted for the ith site location is windward,Predicting the time-before-time for the ith site locationThe historical time of each time passes into the wind, k represents the ordinal number of the time before the predicted time, 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
In the formula,Representing the value of the nth variable normalized by the ith station location and the jth-k predetermined time interval, where k represents the ordinal number of the time preceding the predicted time,Represents an upward rounding, n represents ordinal numbers of the four variables, and sigma n is the weight of the nth variable;
S330, calculating the comprehensive distance S ij between the initial weft wind guess vector and all historical analysis grid point data:
S340, calculating wind field prediction coefficients and utilizing ground altitude Calculating a predicted estimated total number:
Wherein the method comprises the steps of Representing an upward rounding;
Selecting the minimum M distances by utilizing the comprehensive distance S ij, constructing a nearest distance vector Smin im, wherein i represents the position of the ith station, M represents the ordinal number of the nearest distance, m=1..M, and calculating a wind field prediction coefficient omega ij:
n=1 represents the variable ground 10m wind direction, n=2 represents the variable 10m 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:
Using the historical analysis observation dataset Z ij, a 10 meter wind speed Z ij (wsp) and a 10 meter wind direction observation value Z ij (wdir), a historical observed weft wind Zu ij and warp wind Zv ij are calculated according to the following calculation formula:
Zuij＝-Zij(wsp)×sin(Zij(wdir))
Zvij＝-Zij(wsp)×cos(Zij(wdir))
the predicted weft wind Fu i and warp wind Fv i are calculated as:
Wherein i represents the ith station position, and ω ij is the wind field prediction coefficient.
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.
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