23 results about "Wind forecast" patented technology

The invention provides a meteorological radar radial speed feature extraction method for near strong wind forecasting, which comprises the following steps: converting radial speed data into a digital image, obtaining a plurality of retreating / approaching wind speed zones by searching color block contours with different threshold values, and expanding corresponding wind speed zones. And judging whether the speed extreme value and the center distance of the two meet characteristic conditions or not, and finally obtaining radial speed characteristics conforming to convection strong wind. According to the invention, radar radial speed data is converted into a digital image, and image identification and contour extraction are carried out, so that the processing speed is greatly improved; the space continuity of the data is enhanced through a Gaussian blur method, so that the subsequent image recognition is more accurate; the retreating wind speed area and the approaching wind speed area are combined together as a group of wind speed areas for treatment, so that the characteristics of the wind field are reflected more comprehensively; by judging the contour overlapping relation and de-weighting according to the speed value, the feature with the large speed value is reserved, noise interference can be effectively removed, and the reliability of the final extraction result is improved.

Computer implemented systems and methods for providing an uncertainty platform for an energy grid controller that (1) receives risk inputs including (a) one or more load forecasts, (b) one or more wind forecasts, (c) one or more solar forecasts, (d) one or more generator availability risk forecasts, (e) one or more generator fail-to-start or fail-to-run predictions, (f) one or more net scheduled interchange forecasts, and / or (g) one or more transmission congestion forecasts; (2) determines net uncertainty for a predetermined time period based on the risk inputs; and (3) provides dynamic risk outputs including forecast scenarios, reserve requirements and / or reserve margin thresholds based on the determination of net uncertainty.

The invention discloses a shallow wind forecasting method, device and equipment and a storage medium, and relates to the technical field of shallow wind forecasting, and the method comprises the steps: carrying out the training of historical shallow wind observation data of a station, obtaining a first model, and carrying out the forecasting of the shallow wind in a first time period in the future through the first model; and based on the position information of the station, training by using a historical numerical forecasting product to obtain a second model, and forecasting shallow wind in a second time period in the future by using the second model. In this way, according to shallow wind forecast in different time periods, different data are adopted to train the first model and the second model respectively; and in combination with historical observation data of the station and a data forecasting product issued by a forecasting center, the accuracy of shallow wind forecasting can be improved in a targeted manner.

The invention provides a launching site shallow wind speed forecast correction model construction method and system based on a support vector machine, and relates to the technical field of aerospace weather forecast guarantee. According to the method, a launching site 80m-height shallow wind forecasting correction model of different forecasting times and wind speed grades is established based on a support vector machine algorithm by utilizing site live data of a launching site and ECMWF element forecasting data, and the model can integrally improve the forecasting accuracy of 80m-height wind speed.

This invention discloses a reinforcement learning-based rocket load reduction guidance law design method considering the time-varying characteristics of wind fields. It solves the problem that existing rocket load reduction guidance law designs cannot adapt to the deviation between actual and predicted wind fields, belonging to the field of rocket load reduction guidance and control. The method includes: generating high-resolution hourly upper-level wind forecast data; constructing a Markov decision process model for the ascent phase guidance; using the high-resolution hourly upper-level wind forecast data to simulate the uncertainty deviation between the predicted and actual wind fields through the model; completing the interactive sampling of the entire round of the ascent phase guidance Markov decision process using a rocket load reduction guidance strategy; calculating the update gradient based on the sampled data; updating the parameters of the rocket load reduction guidance strategy using the update gradient; optimizing the uncertainty deviation; and obtaining a rocket load reduction guidance law that minimizes the expected maximum aerodynamic load throughout the ascent phase. This invention enables real-time correction of rocket attitude commands when there is a deviation between the predicted and actual wind fields.

The invention relates to the technical field of weather forecast, and particularly discloses a method for forecasting the strong wind intensity grade of an intelligent grid by using a convection potential index, which comprises the following steps of: firstly, acquiring a numerical mode product with a specified range and resolution, extracting five elements, namely a lifting index, 1000m vertical wind shear, a temperature difference between 500hPa and 850hPa, a multilayer vertical speed and convection effective potential energy, and calculating the wind intensity grade of the intelligent grid by using the convection potential index; the quartile value, the abnormal value and the like of each element are respectively calculated, and a standardized value is obtained by combining the correlation with the convection strong wind. The standardized values of all the elements are summed to obtain a convection gale potential index, then the gale intensity is calculated by fusing the maximum wind speed and the maximum wind speed, and after potential index correction is conducted, the grade of the gale intensity is determined by adopting a dual-threshold grading method in combination with rainfall elimination and control. According to the method, the instantaneous and continuous strength of the strong wind is considered, the convection potential effect is fused, strong wind forecasting skills of different time periods and the accurate forecasting advance of the local strong wind are improved, and the TS score of the forecasting result is obviously improved compared with an EC numerical mode on the whole, especially for high-grade strong wind forecasting.

The application discloses a kind of extreme gale forecasting methods, systems, equipment and medium for new energy station, comprising: obtaining the basic information of new energy station and grid numerical weather prediction conventional data;According to the basic information of the new energy station and grid numerical weather prediction conventional data, calculate the wind speed u h At each integration time step on the hub height h of wind turbine of new energy station in forecast period;According to the wind speed u h At each integration time step on the hub height h of wind turbine of new energy station in forecast period, calculate the extreme gale wind speed u gust At each integration time step on the hub height h of wind turbine of new energy station in forecast period;According to the extreme gale wind speed u gust At each integration time step on the hub height h of wind turbine of new energy station in forecast period, determine the forecast wind speed of extreme gale of new energy station in forecast period, which can accurately and reliably forecast extreme gale for new energy station.

The invention belongs to the technical field of electric power weather forecast, and discloses a transverse wind prediction method and system for an overhead transmission line, and the method comprises the steps: obtaining the position and line height information of the overhead transmission line, and numerical weather forecast data, and calculating the longitudinal wind and the latitudinal wind predicted at the line height; establishing a coordinate system in which the power transmission line is coordinated with the numerical forecasting wind field; constructing an analysis model for forecasting the wind direction and the trend of the power transmission line section by combining the coordination coordinate system, the warp-direction wind and weft-direction wind data at the line height and the position information of the overhead power transmission line; and based on the analysis model for forecasting the wind direction and the trend of the power transmission line section, combining the longitudinal wind and the latitudinal wind forecasted at the height of the line, establishing a transverse wind size forecasting model for the line section, and forecasting the transverse wind of the overhead power transmission line. According to the method, the warp wind and the weft wind at the height of the power transmission line can be calculated more accurately, so that accurate basic data is provided for subsequent wind direction and transverse wind calculation.

The application discloses a kind of minute level low altitude three-dimensional wind forecasting method and device based on WRF-CALMET, including according to GFS global forecast data and Guangdong 3km forecast data, the initial field file and boundary condition file of WRF mode are obtained;According to the initial field file and boundary condition file, the first input file of CALMET mode is obtained by forecasting through WRF mode;According to topographic elevation data and land use data, the second input file of CALMET mode is obtained;According to the first input file and the second input file of CALMET mode, the minute level low altitude three-dimensional wind forecasting result is obtained by running CALMET mode in multiple processes;Wherein, the time length of the minute level low altitude three-dimensional wind forecasting result is 6 hours, and the output time interval is 6 minutes, and the spatial resolution is 100 meters.Compared with prior art, the application can improve the accuracy and timeliness of fine low altitude three-dimensional wind forecasting.

The invention belongs to the technical field of data processing, and particularly relates to a thunderstorm and gale classification forecasting method and system.The method comprises the steps that radar observation data, numerical mode output and ground observation data are obtained, and a unified grid multi-source data set is obtained through preprocessing and space-time registration; extracting radar morphological characteristics through a convolutional neural network, and identifying a thunderstorm and gale overall risk area in combination with environmental factors output in a numerical mode; then extracting local radar features in the region, estimating the cold pool strength and obtaining a time sequence mutation signal; and finally, inputting the three types of information into a classification model, outputting two types of risk judgment results of common thunderstorm gale and downburst, and presenting the results in probability and uncertainty intervals. According to the method, the downburst can be accurately identified, and the refinement level of medium and short time thunderstorm gale forecast is improved.

The invention discloses a 10-meter wind forecasting method and system, and the method comprises the steps: collecting meteorological data, and extracting feature parameters from the collected meteorological data; establishing a corresponding relationship between the characteristic parameters and 10-meter wind, and taking data of the established corresponding relationship as a first training data set; dividing the first training data set into a plurality of training data sets, and inputting one training data set for each base model in a first layer in the Stacking model; a prediction result output by each base model is obtained, the prediction results serve as a second training data set to be input into a meta-model of a second layer in the Stacking model to be trained, a trained Stacking model is obtained, the meta-model of the second layer is a lasso regression model, and the trained Stacking model is used for outputting a prediction value of 10-meter wind. According to the method and the device, the problems of errors and uncertainty in predicting 10-meter wind by adopting a single algorithm in the prior art are solved, and the accuracy of 10-meter wind prediction is improved to a certain extent.

The invention discloses a thunderstorm gale short-time nowcasting method based on an ENTwo evolution network and Uform network hybrid architecture, and the method comprises the steps: obtaining historical live data, which comprises a radar echo sample data set, a maximum wind speed data set and a lightning data set; generating a grid thunderstorm gale label data set based on the gale wind speed and the lightning data; an ENTwo evolution network and a Uform hybrid model are constructed, the evolution network performs space-time extrapolation on the input data to generate a forecast field, and the Uform fuses an extrapolation result and historical data to output a thunderstorm and gale forecast field; training the model through a loss function; and inputting real-time data into the training model, and outputting a lattice forecasting field. According to the method, the accuracy and timeliness of 0-2-hour thunderstorm gale forecasting are effectively improved, the spatial resolution reaches 1 kilometer, the strong echo and extreme wind speed forecasting performance is superior to that of a traditional method, and high-precision technical support is provided for disaster prevention and reduction.

The invention provides a deep learning-based severe convection strong wind short-term and imminent forecasting method and device, and the method comprises the steps: inputting historical data into an encoder, obtaining an encoding result of the historical data outputted by the encoder, and enabling the historical data to comprise historical radar echo data and historical automatic station data; inputting the coding result into a MaxViT module to obtain an intermediate feature of the historical data output by the MaxViT module; inputting the coding result and the intermediate feature into a feature fusion module to obtain a fusion feature of the historical data output by the feature fusion module; the fusion feature is input into a decoder, a decoding result of the fusion feature output by the decoder is obtained, and the encoder and the decoder comprise a Swin Transformer module; and according to the decoding result, obtaining a short temporary gale forecast result. According to the invention, the precision and efficiency of short-term and temporary strong wind forecasting are improved.

This application provides a photovoltaic (PV) panel tilt angle optimization method and apparatus based on data fusion analysis. It assesses the power generation revenue and risk losses of a PV power station when the target PV panel is at its optimal tilt angle at different time intervals, based on the optimal tilt angle and strong wind forecast results at different time intervals, thereby ensuring the stability and safety of the system under harsh weather conditions. Then, based on the power generation revenue and risk losses of the PV power station when the target PV panel is at its optimal tilt angle at different time intervals, and the actual situation of the PV power station, it calculates the optimal tilt angle adjustment strategy that maximizes the overall revenue and minimizes the risk of the PV power station. This application, by dynamically adjusting the PV panel tilt angle, not only optimizes energy harvesting efficiency but also significantly enhances the structure's wind resistance, achieving dual optimization of energy and safety, and contributing to the development and application of sustainable energy technologies.

The invention discloses a strong wind forecasting method based on a probability density matching method and a micro-scale numerical mode, which comprises the following steps of: acquiring meteorological data, processing the meteorological data to obtain a resolution meteorological forecasting field, and taking the resolution meteorological forecasting field as an initial field; fine adjustment is performed on the initial field through a terrain dynamics effect, a downhill airflow characteristic effect, a terrain blocking effect and radiation minimization to obtain wind speed forecast data, a maximum wind speed forecast value and a simulated wind speed are obtained based on the wind speed forecast data, a joint probability density function is established based on the maximum wind speed forecast value and the simulated wind speed, and the maximum wind speed forecast value and the simulated wind speed are calculated. And correcting the maximum wind speed forecast value through the established joint probability density function. According to the method, the microscale meteorological mode CALMET and the mesoscale meteorological mode WRF are combined, and the NCEP-GFS data are utilized, so that high-resolution gale forecasting of the complex terrain area is realized.

This application provides a radar echo extrapolation and thunderstorm / strong wind data processing method, apparatus, and electronic device based on optical flow, relating to the field of meteorological forecasting. The method includes: acquiring radar base data of a target area over a continuous historical period to generate a corresponding radar reflectivity factor image sequence; inputting the preprocessed radar reflectivity factor image into an optical flow calculation model to obtain a dense optical flow field; calculating the vorticity field and divergence field based on the dense optical flow field; stitching and fusing the radar reflectivity factor image, optical flow field, vorticity field, and divergence field at the same historical moment to form a multi-feature fused spatiotemporal sequence data; inputting the spatiotemporal sequence data into a pre-trained spatiotemporal sequence prediction model to output prediction results for a future preset time period; and generating and outputting a short-term thunderstorm / strong wind forecast product. This method can more accurately capture the nonlinear evolution of the micro-scale structure of thunderstorm / strong wind systems, significantly improving the accuracy of extrapolation forecasts.

The invention discloses a method and device for determining a drag coefficient of terrain turbulence drag force, equipment and a medium, and relates to the field of weather forecast. Comprising the following steps: determining the surface roughness as a first dependence parameter of wind shear, and determining the Monin-Obchhoff length as a second dependence parameter of wind shear; determining the current value of the first proportion according to the current parameter value of the first dependency parameter and the current parameter value of the second dependency parameter; determining a current drag coefficient based on the drag coefficient constant, the current value of the first proportion and an exponential function associated with the first proportion; the current drag coefficient is used for determining the current terrain turbulence drag force, and low-altitude wind forecasting is carried out based on the current terrain turbulence drag force, so that the low-altitude wind forecasting accuracy of the numerical weather forecasting mode is improved by considering the effect of the boundary layer stability on the terrain turbulence drag force.

The application discloses a terrain turbulent drag force drag coefficient determination method and device, equipment and medium, and relates to the meteorological forecast field. Including: the ground roughness is determined as the first dependent parameter of the wind shear, and the Monin-Obukhov length is determined as the second dependent parameter of the wind shear; according to the current parameter value of the first dependent parameter and the current parameter value of the second dependent parameter, the current value of the first proportion is determined; based on the drag coefficient constant, the current value of the first proportion and the exponential function associated with the first proportion, the current drag coefficient is determined; the current drag coefficient is used to determine the current terrain turbulent drag force, so as to carry out low-altitude wind forecast based on the current terrain turbulent drag force, thereby improving the accuracy of the numerical weather prediction model for low-altitude wind forecast by considering the effect of the boundary layer stability on the terrain turbulent drag force.

The invention provides a plateau canyon gale forecasting method based on a WSTD-KOA-BPNN technology. The method comprises the following steps: acquiring historical wind speed data and other meteorological element data such as temperature of a plateau canyon target area by using a distributed wind speed sensor; missing data points are filled through a cubic spline interpolation method, noise filtering is carried out through a wavelet soft threshold denoising algorithm (WSTD), and normalization processing is carried out on the denoised wind speed data through a Min-Max method; parameters of a back propagation neural network (BPNN) are optimized based on a Kepler optimization algorithm (KOA), and a KOA-BPNN prediction model is constructed; and real-time monitoring and forecast result output. According to the method, WSTD noise suppression, KOA global parameter optimization and BPNN nonlinear modeling capability are fused, so that the capture precision and generalization performance of the plateau canyon complex gale features are remarkably improved, and reliable technical support is provided for disaster prevention and reduction.

The present invention discloses a method and device for verifying and analyzing large-scale thunderstorm gale events. First, a large-scale thunderstorm gale event data set is constructed, including historical reanalysis data, dual-polarization radar observation data, satellite observation data, ground automatic weather station observation data, and convective-scale numerical prediction model simulation results. The quality control of the dual-polarization radar data and ground station observation data is carried out, and they are interpolated and divided into model grids. The structure, intensity, and evolution process of the large-scale thunderstorm gale storm system predicted by the model are compared and verified. Using the judgment criteria for large-scale thunderstorm gales, the intensity grading and horizontal distribution range of the gales, the wind force intensity of the gale disasters, the propagation path trajectory and moving speed of the large-scale thunderstorm gales are verified. The present invention can effectively identify and improve the accuracy of large-scale thunderstorm gale forecasts, and has certain guiding significance for reducing the forecast and early warning of strong wind disasters.

The application discloses a thunderstorm gale prediction method and system based on centralized sampling and multi-branch fusion, and equipment, relates to the meteorological prediction technical field, and includes: adopting centralized sampling to intercept the radar echo sequence of a prediction area; performing multi-branch feature extraction on the radar echo sequence to obtain double-dimension splicing features, and performing branch feature interaction on the double-dimension splicing features to obtain global fusion features; extracting physical constraint features of the prediction area, and constructing physical-global fusion features based on the physical constraint features and the global fusion features; performing coordinate regression and logistic regression calculation based on the physical-global fusion features to obtain the thunderstorm gale occurrence probability of the thunderstorm gale drop point in a short term. The application is used to solve the problem that the thunderstorm gale drop area and the thunderstorm gale occurrence probability in a short term cannot be accurately predicted.

The application relates to the technical field of power system safety protection, and discloses a strong wind prediction method, system, device and medium for a power grid transmission channel, global meteorological features and local topographic features are respectively constructed based on meteorological prediction data and geographic information data of the power grid transmission channel, the obtained two features are input into a composite neural network model constructed based on a convolutional neural network and FiLM modulation for processing, and a wind field change prediction value of the power grid transmission channel under a first spatial resolution is output; based on real-time wind speed data of the power grid transmission channel, an initial moment wind field of the power grid transmission channel under a second spatial resolution is determined by using a double-source distance weighted interpolation method based on terrain correction; a change amount at a prediction moment and a wind value at an initial moment are subjected to vector composition to form a strong wind prediction result; and a km-level spatial resolution, 1-24 hour short-time refined strong wind prediction for a transmission channel in a complex terrain area is realized.

The invention discloses a minute-level low-altitude three-dimensional wind forecasting method and device based on WRF-CALMET, and the method comprises the steps: obtaining an initial field file and a boundary condition file of a WRF mode according to GFS global forecasting data and Guangdong 3km forecasting data; forecasting through a WRF mode according to the initial field file and the boundary condition file to obtain a first input file of a CALMET mode; obtaining a second input file of the CALMET mode according to the terrain elevation data and the land utilization data; according to the first input file and the second input file of the CALMET mode, the CALMET mode is operated in a multi-process mode, and a minute-level low-altitude three-dimensional wind forecast result is obtained; wherein the duration of the minute-level low-altitude three-dimensional wind forecast result is 6 hours, the output time interval is 6 minutes, and the spatial resolution is 100 meters. Compared with the prior art, the accuracy and timeliness of refined low-altitude three-dimensional wind forecast can be improved.