169 results about "Numerical weather prediction" patented technology

A wind energy forecasting method with extreme wind speed prediction function cooperated with a central computer, comprising the steps of: inputting a weather data which contains a numerical weather prediction data; implementing a modification with a first model output statistics; implementing a modification with a physical model in accordance with the output of the first model output statistics that can iteratively calculate the results by varying the angles of wind direction; implementing a modification with a second model output statistics; and implementing a prediction of extreme wind speed caused by typhoon, which comprises the following sub-steps of: using a wind and typhoon database to find track data of plural historical typhoons within a certain distance from a target typhoon; using an extreme wind and wind energy prediction tool to calculate at least one extreme wind speed in the future of the target typhoon and calculate the probability of occurring the extreme wind speed; and modifying the extreme wind speed with the physical model to the extreme wind speed at the position or height of a wind turbine.

The invention provides an ultra-short term wind power station generated power forecasting system, which belongs to the technical field of automatic scheduling of wind power plants. The system comprises a real-time wind measurement data server, a numerical weather prediction server, a wind power station real-time operation server, an ultra-short term forecasting processing server and data acquiring equipment, wherein the numerical weather prediction server is used for acquiring weather prediction data of a near-earth layer at the position of a wind power station; the real-time wind measurementdata server is used for acquiring wind speed data, wind direction data, temperature data, air pressure data and humidity data at the position of a wind measurement tower; the wind power station real-time operation data server is used for acquiring the total generated power data of the wind power station; and the ultra-short term forecasting processing server is used for forecasting the total generated power data of the wind power station at the next moment. The system solves the technical problems that future generated power variation tendency cannot be tracked and multi-step forecasting accuracy is relatively low in the prior art.

The invention relates to a large grid real-time scheduling method for accepting access of wind power, which belongs to the technical field of power system operation and control. The method comprises: according to the characteristics of wind power and load fluctuation and the control characteristics of generator sets, classifying the generator sets in the whole grid; acquiring the current plan, real-time output, connecting line plan, numerical weather prediction and other related information of each generator set; performing ultrashort period load prediction and wind power output prediction towork out the output regulation amount at next moment of real-time scheduling generator sets and construct an active real-time scheduling model with the smallest wind loss; working out the output regulation amount of the real-time scheduling generator sets including the wind power generator sets by using a simplex method; and transmitting the next output regulation amount of thermal power generator sets directly, and giving the output maximum value at next moment of the wind power generator sets. In the invention, the combined optimized scheduling of the wind power generator sets and traditional generator sets is performed to eliminate wind power generation prediction deviation and load prediction deviation in advance, so the wind power accepting capability of a power grid is improved to the maximum extent while the operation economy of the power grid is guaranteed.

The invention discloses a physical prediction method for wind power station power based on a computational fluid mechanics model. The physical prediction method comprises the following steps: establishing a computational fluid mechanics model; performing discretization on wind conditions of a wind power station, and taking the discretized wind as boundary conditions for conditions numerical simulation of the computational fluid to obtain space flow filed distribution of the wind power station at the discretized wind conditions; establishing data base of hub height, wind speed, wind direction and generated power of wind generation sets under the discretized wind conditions; and taking numerical weather prediction parameters as input data, and utilizing the data base to figure out the wind speeds and the wind directions of the wind generation sets so as to figure out the generated powers of the wind generation sets, and accordingly obtain predicted value of the wind power station power. According to the invention, the physical prediction method is applicable to multiple wind power stations, has small calculated amount in the power prediction stage and has short calculation time.

The invention discloses a wind resource assessment method based on numerical weather prediction and the computational fluid dynamics. According to the method, area wind speed situations of a wind power plant in a selected year are simulated in a numerical weather prediction model, so that numerical weather prediction results comprising wind speed and wind direction time variation sequences of the wind power plant are obtained; one or more wind speed and wind direction time variation sequences are selected from the numerical weather prediction results and input in CFD software of the computational fluid dynamics, and wind resource situations of the whole wind power plant can calculated and obtained. Compared with a method of combining a meso-scale numerical model and a micro-scale numerical model, the wind resource assessment method achieves more accurate physical solution and calculation of the wind power plant on a micro level, and meanwhile the effects on wind speed attenuation or turbulence from complex terrain and wake effects are considered. Compared with wind resource assessment conducted through the CFD software only, the method can provide input of more result points of the CFD software by being combined with results of the numerical weather prediction model.

The invention discloses a wind power station power projection method based on error statistics modification. The procedures adopted by the invention is that firstly, data pretreatment is performed, the projection and measured data and the statistical data of the wind power station are divided into a plurality of sample sets according to different terrain heights; secondly, the sample sets are used for the training of a wind power projection model so as to form a 24-hour output short-term forecast model of the wind power station, the error distribution condition of the power can be projected through analyzing the projected power value and measured power value so as to obtain the expected value of the power projection error of the wind power station; thirdly, the power projection value of the wind power station can be obtained according to the numerical weather prediction data, weather forecast data and the trained model; and finally, the final power modification value of the wind power station can be obtained through modifying the power projection value according to the projection error statistical calculation result of the wind power station.

The invention provides a super short-period wind power prediction method. The method mainly comprises the following steps: S10, obtaining a wind power prediction result based on numerical weather prediction (NWP) and a wind power prediction result based on wind power farm historic / real time data, and correcting the error of the wind power prediction result based on the NWP in real time; S20, adopting an empirical distribution model, respectively building a prediction error absolute value probability distribution based on an NWP power prediction method at the time and a prediction error absolute value probability distribution based on a wind power farm historic / real time data prediction method; step 30, calculating the wind power prediction result based on the NWP at the time in a prediction time period and a weighting coefficient of the prediction result based on the wind power farm historic / real time data, and obtaining the prediction result of the time; and repeating step S20 and S30 until completing all the prediction time period, and accordingly, obtaining the prediction result of the prediction time period.

The invention discloses a medium-range forecast system and method for cold, rainy and snowy weather based on atmospheric variable physical decomposition. By utilizing the atmospheric space three-dimensional multivariable and time continuous grid data outputted in the past observed, the current observed and the medium-range numerical weather prediction modes, and the physical decomposition of the climate and the weather disturbance quantity, a causality between the regional sustained low temperature, rain and snow and freezing extreme weather events and the weather disturbance prior to and during the events is established in the method. According to the invention, a physical decomposition of the climate seasonal variation field and the day-to-day weather disturbance field is carried out according to the data of day-to-day three-dimensional space grid point temperature, altitude and wind and the like of the atmosphere from the troposphere to the stratosphere in the northern hemisphere for nearly 30 years, and a database for the atmospheric climate field and the weather disturbance field can be established; and a historical database of the low temperature, rain and snow and freezing weather in south China is established according to the data of day-to-day temperature and precipitation from the national meteorological station for nearly 50 years. The invention provides not only an inquiring system for historical low temperature, rain and snow and freezing weather events, but also a forecasting system for medium-range low temperature, rain and snow and freezing weather events.

The invention provides a fusion method and device of a multisource sea surface wind field. The fusion method comprises the following steps: obtaining multisource sea surface wind field data which comprises sea surface wind field data and / or multiple pieces of reanalysis meteorological sea surface wind field data collected by a plurality of satellite borne microwave remote sensors; according to a preset temporal-spatial resolution, independently carrying out meshing processing on the sea surface wind field data obtained by each satellite borne microwave remote sensor to obtain multiple pieces of corresponding sea surface wind field data with the equal longitude and latitude; and utilizing a temporal-spatial interpolation algorithm to carry out interpolation calculation on all sea surface wind field data with the equal longitude and latitude to obtain fusion sea surface wind field data. The fusion method can perform the advantage of the cooperative observation of a multisource satellite, can effectively improve the coverage range and the temporal-spatial resolution of the sea surface wind field data through the fusion sea surface wind field data constructed by the fusion of satellite remote sensing wind field data and / or reanalysis meteorological sea surface wind field data on a premise that meso-and micro-scale characteristic information can be kept, and can better meet the requirements of numerical weather prediction, marine forecasting research and marine meso-and micro-scale system research.

The invention discloses a method for predicting wind speed and power of a wind farm based on wavelet decomposition and a support vector machine. The method comprises: acquiring wind speed and power historical data of a whole wind farm in a preset time, to obtain a historical wind speed time sequence and a historical power time sequence of the wind farm; using a wavelet packet decomposition technology to perform wavelet packet decomposition on the historical wind speed time sequence, to obtain a low-frequency stage component, a middle-frequency stage component, and a high -frequency stage component of the historical wind speed time sequence; using a grey support vector machine prediction model to predict each component of the historical wind speed time sequence, and then using wavelet packet reconstruction to obtain short-period wind speed prediction data; using historical wind electricity power data and numerical weather prediction wind speed data as a training set to establish a grey support vector machine model, predicting wind electricity power; predicting the obtained wind speed prediction data and the wind electricity power prediction data through a RBF neural network, to obtain a final prediction value of the wind electricity power.

The invention discloses a cloud platform-based large-scale centralized remote wind electric power prediction center, which is built in a wind electric power prediction system on an enterprise-level personnel cloud platform, and comprises a cloud real-time historical database system, a relational database, a downscaling large-scale parallel mode computation module for numerical weather prediction, a model computation module, a statistic analysis module, a data input / output interface and a man-machine interaction interface. In the prediction center, the traditional wind electric power prediction system is transferred onto the enterprise-level personnel cloud platform, the downscaling large-scale parallel mode computation module for numerical weather prediction is additionally arranged, so services can be provided for multiple wind farms through the strong and flexible computation capability and storage capability. While the safety is ensured, the maximum resource intensification, rapid implementation of system deployment and the minimum expenses of maintenance cost are realized, and the prediction center can be suitable for the severe environments of future offshore wind farms.

The invention discloses a power grid weather predicting and early-warning system based on a numerical weather prediction model. The power grid weather predicting and early-warning system comprises a numerical weather predicting system, a micrometeorological data collection system, an MOS predicting system and a weather predicting system, the micrometeorological data collection system is connected with the MOS predicting system, the numerical weather predicting system is connected with the MOS predicting system and the weather predicting system, the MOS predicting system is connected with the weather predicting system, and the weather predicting system is used for conducting predicting, early-warning, querying, counting and analyzing on weather phenomena of an area under administration and providing a visualized graph interface by taking data obtained by the numerical weather predicting system and data obtained by the MOS predicting system as bases. The power grid weather predicting and early-warning system based on the numerical weather prediction model has the advantage that quantitative prediction can be conducted automatically without relying on external numerical weather predictions.

The invention discloses a photovoltaic power station generation power prediction method comprising the steps that six meteorological characteristics are daily extracted by using the historical meteorological data of a photovoltaic power station so that a meteorological characteristic library is established; the daily characteristic data in the meteorological characteristic library are clustered through a KFCM algorithm so as to realize weather type classification, and class marking is performed on the daily power data and the meteorological data; an SVR sub-model is established for each classof power data and meteorological data according to the class mark; the weather type of the target day is identified by using the SVM through the target day weather characteristics provided by numerical weather prediction and the corresponding SVR sub-model is selected; an ARIMA model is established by using the real-time monitoring data of the target day, and real-time prediction of the irradiation intensity and the temperature can be realized by using the rolling prediction model; and the prediction values of the irradiation intensity and the temperature are inputted to the selected SVR sub-model so that the photovoltaic power station power prediction result can be obtained. The photovoltaic power station generation power prediction accuracy can be enhanced.

The invention belongs to the technical field of a power system and particularly relates to an integrated platform system for remote management and control of a wind power field cluster for cross-regional multiple-wind-field unified management and control. The integrated platform system for the remote management and control of the wind power field cluster comprises a remote wind power field monitoring subsystem, a wind power prediction sub system, a video image monitoring subsystem and a large screen projection display subsystem, wherein subsystems are in communication connection with another other through a communication network; the remote wind power field monitoring subsystem is used for acquiring the operation data of wind power field booster station monitoring, box transformer substation monitoring and real-time fan monitoring; the wind power prediction subsystem is used for downloading numerical weather prediction information, receiving the data of a wind power field anemometer tower, performing wind power field output prediction on each wind power field in an ultrashort period of future 0-4 hours and short period of 0-72 hours; and the wind power prediction subsystem is further used for giving early warning on disaster weather. The integrated platform system for the remote management and control of the wind power field cluster is capable of realizing cross-regional, multi-wind field state monitoring and operation management and realizing the wind power prediction, state detection and fault treatment of the full-digital wind power fields.

The invention provides a numerical weather prediction method based on a mixed ambient field. According to the method, a mixed data ambient field of numerical weather prediction is generated based on the existing data of the relevant weather data and forecast data; initial parameters required in establishing a WRF mode are set to complete initial establishment of the WRF mode; effective meteorological data on feature level grid of the WRF mode is obtained; in the WRF mode, conclusion parameters of the numerical weather prediction is obtained by calculation according to the effective meteorological data; the conclusion parameters are extracted and processed to obtain conclusion data diagram of the numerical weather prediction. The present invention provides the mixed data using the reanalyzed data and the predicted background field as the background data for driving the meso-scale numerical weather prediction for numerical weather prediction; as the reanalyzed data itself assimilates a large number of observation data and the accuracy is high, which can significantly improve the accuracy of numerical weather prediction and save a lot of computing resources.

The invention designs a numerical weather prediction-based early warning method for windage yaw flashover of a power transmission line. The method comprises the steps of firstly, performing grid point forecast wind velocity mapping processing by adopting an inverse distance weighted interpolation algorithm, and finishing a wind velocity component perpendicular to the line according to an included angle between a wind direction and a line route; secondly, calculating a minimum air gap between the line and a pole tower by applying a rigid straight bar method by taking a typical cup tower as an example; and finally, performing air density, humidity and precipitation correction on a gap breakdown voltage to obtain a flashover voltage value under a forecast weather condition, and comparing the flashover voltage with a system running voltage to finish graded early warning of the windage yaw flashover. The method has an important practical significance for wind prevention and disaster reduction work of a power grid.

The invention provides a wind power probability forecasting method based on numerical weather forecasting ensemble forecasting results. Numerical weather forecasting serves as the foundation, basic input data are provided for short-period wind power forecasting through a numerical weather forecasting ensemble forecasting technology, and a short-period forecasting model is established for each ensemble member to obtain a plurality of groups of forecasting results. For the obtained plurality of groups of forecasting results, and different characteristic forecast errors are classified through an ensemble forecasting configuration characteristic classification method and a forecasting power level classification method to obtain future forecast error bands under certain confidence level. According to the wind power probability forecasting method, under the same confidence level, the error band section is narrower, and for power grids containing large-scale wind power integration, under the condition of the same safety margin, the power grid operation cost can be effectively reduced, and the power grid operation economical property can be improved.

A wind power prediction apparatus and a prediction method are disclosed. The prediction apparatus consists of a fan data test device, a historical numerical weather prediction data interface, a data acquisition device, a router, a database, a data processor and a network server. According to the wind power prediction apparatus and method, a non-stable wind speed time sequence is decomposed into components of different scales through empirical mode decomposition, then corresponding models are established for different components respectively to perform prediction, and prediction results of all the components are combined through improved weighted prediction; and compared to a wind power modeling prediction method in the prior art, the prediction accuracy can be effectively improved.

The invention provides a method for predicting wind power plant short-term power. The method includes the steps of firstly, setting up a wind speed statistical downscaling model of a single draught fan; secondly, generating a predicted wind speed at the height of a hub in the position where the single draught fan is located according to predicting factors of mesoscale numerical weather prediction for a wind power plant area in 48 hours and the statistical downscaling model of the single draught fan in the first step; thirdly, setting up a wind speed, wind direction-power model of each draught fan, and obtaining power prediction of each draught fan according to a wind direction predicted through the mesoscale numerical weather prediction and a wind speed predicted in the second step. According to the method, the uncertainty caused by lack of mesoscale resolution is effectively reduced, and the prediction accuracy of the wind power plant short-term power is remarkably improved.

The invention provides a direct assimilation method of MODIS satellite data. The direct assimilation method of the MODIS satellite data includes the steps that the MODIS satellite data are pre-processed; the MODIS satellite data are detected; an assimilation satellite data set is determined; a radiation transmission mode is optimized; the MODIS satellite data are assimilated. Simulation of the radiation transmission mode on the earth surface radiation process is improved, the accuracy of simulating the atmosphere movement inside a boundary layer by a numerical mode is improved, and the accuracy of numerical weather prediction data for wind power forecast is further improved.

The invention discloses a universal wind power station historical data preprocessing method and device for wind power prediction. The universal wind power station historical data preprocessing method comprises the following steps of: forming NWP (Numerical Weather Prediction) data and a statistical fit curve of actually-measured power of a wind power station by adopting a statistical fit method, and eliminating remarkable power abnormal points caused by electricity limiting, NWP fault and the like under the condition of considering a certain error range; and judging whether expansion exists in the wind power station according to the comparison between the actually-measured power of the wind power station as well as the statistical fit curve and corresponding historical data, and selecting a corresponding sample updating frequency to ensure that effective samples are concentrated to serve as latest effective data of the wind power station. The data preprocessing device for wind power prediction, disclosed by the invention, can be conveniently integrated into various wind power prediction systems and further improves the prediction precision, the engineering practicability and the self-adaptive capability of the wind power system.

The invention discloses a power generation output power prediction system of a photovoltaic power station. The power generation output power prediction system comprises a database server, a numerical weather prediction processing server, a power prediction server and a user interface server, wherein the database server is used for performing storage and data interaction of data; the numerical weather prediction processing server is used for processing numerical weather prediction data downloaded from a network to generate meteorological element data of a prediction time interval at the place where the photovoltaic power station is positioned and transmitting the meteorological element data into the database server; the power prediction server is used for calling the meteorological element data from the database server, obtaining power generation output power of the photovoltaic power station at the prediction time interval corresponding to the meteorological element data according to relation of the meteorological element data and the output power and transmitting a result into the database server for storage; and the user interface server is used for calling a processing result of the power prediction server from the database server to realize interaction with a user. The system provided by the invention is simple and practical and has high accuracy.

The invention discloses a fault early warning method of a gearbox of a wind turbine generator. The early warning method mainly includes the following steps of data acquisition, wherein a terminal acquires the historical data of wind turbine generator power, environment temperature and wind speed through a hard contact temperature sensor, a soft contact temperature sensor and a temperature acquisition card arranged in the gearbox or calls the historical data of wind turbine generator power, environment temperature and wind speed from wind power plant recorded data, and the time span of the datacovers the whole fault evolution section; data processing, wherein input data is normalized, the integrity of the input data is verified, abnormal data and incomplete data are deleted from the inputdata, a BP neural network including an AIS-SA hybrid network prediction algorithm is established, and then the network size and various initial connection weights and thresholds are determined; earlywarning calculation, wherein the acquired temperature data is predicted through the optimized network, the wind speed is subjected to approximate substitution in combination with numerical weather prediction, a temperature prediction conversion curve in a certain future time is obtained, the residual of predicted temperature and actual temperature is calculated, and with several recording points as an early warning section, the residual mean and standard deviation of the two temperatures in the section are worked out.

The present invention relates to an ensemble wind power forecasting platform system and the operational method thereof. According to the present invention, a great amount of wind energy predictions from multiple sources, including numerical weather prediction information, multi-grid prediction information, and multiple wind-energy predicting methods, are integrated and processed for providing users with an ensemble prediction. Thereby, the trend and the possible variation range of the output capacity of a wind farm can be mastered. In addition, by means of the integration platform, the predicted results by different prediction modes can be compared and the history data and the predicted results can be compared as well, which can be used as a basis for improving modes for prediction-mode developers.

The invention provides a day-ahead wind speed multistep prediction method fused with numerical weather prediction, and belongs to the technical field of wind speed prediction. The problems that an existing method is directly fused with numerical weather prediction, the prediction error is large, and the prediction precision of wind electricity power is low are solved. According to the technical scheme, the method comprises the steps of analyzing the information effectiveness of an original wind velocity sequence, determining the predictability time duration of a statistic forecasting model, obtaining the wind speed prediction result within a predicable time duration, conducting information effectiveness analysis on a numerical weather prediction model, determining the predicable time duration of the numerical weather prediction model, obtaining the wind speed prediction result within the predicable time duration, establishing a day-ahead wind speed fusion prediction model according to the wind speed prediction result and predicting the actual wind speed. The day-ahead wind speed multistep prediction method fused with numerical weather prediction is suitable for predicting the wind speed within day-ahead 24 hours in the future.

The invention discloses a wind power plant short-term wind power prediction system in view of an operating state of a wind power plant draught fan. The wind power plant short-term wind power prediction system mainly comprises a super-short-term wind power output prediction module, a numerical weather prediction obtaining and analyzing module, a data collecting platform and a wind field real-time information collecting module, wherein the numerical weather prediction obtaining and analyzing module, the data collecting platform and the wind field real-time information collecting module are respectively connected with the super-short-term wind power output prediction module. The wind power plant short-term wind power prediction system in view of the operating state of the wind power plant draught fan can overcome the defects that in the prior art, prediction parameters are not comprehensive and prediction accuracy is low, and can achieve the advantages of being comprehensive in predication parameter and high in predication accuracy.

The invention discloses a method for carrying out fusion processing on meteorological data and generating a numerical weather forecast, which comprises the following steps: step 1, collecting and receiving various meteorological element data from meteorological observation detection equipment or CCTV to obtain a meteorological element set X1; step 2, performing standardized preprocessing on the acquired meteorological element data to obtain a standardized meteorological element set X; step 3, performing three-dimensional data variation assimilation on the meteorological element set X, performing characteristic error analysis and cross covariance analysis on the data in the meteorological element set X to obtain a covariance error root mean square of each data in the meteorological elementset X, and obtaining an optimal meteorological element data type according to the covariance error root mean square; step 4, performing WRF numerical weather forecast mode compiling according to the optimal meteorological element type; and step 5, performing forecast post-processing on the WRF mode result to generate meteorological element numerical forecast.

The invention provides an intelligent wind power prediction system, which comprises a mesoscale numerical value simulation module, a micro-scale numerical value simulation module, a generating capacity physics calculation module and an error correction statistics module, wherein the mesoscale numerical value simulation module is used for predicting wind power according to the actual requirements of a weather climate characteristic set wind power plant; the micro-scale numerical value simulation module is used for carrying out downscaling processing on a wind power prediction result obtained by a mesoscale numerical value simulation system; the generating capacity physics calculation module is used for calculating the generating capacity of each machine position according to a wind profile result generated in the micro-scale numerical value simulation module; and the error correction statistics module is used for building an error correction model through analyzing and computing an error between the prediction result and the actual generating capacity. According to the intelligent wind power prediction system, the functions of short-term and super-short-term multi-mode multi-scale wind power prediction, wind speed / wind power prediction fused with numerical weather prediction, wind power integration stable control, wind power curve reporting and optimizing and the like can be achieved.