CN102768738A - Method and device for power predication of wind power station - Google Patents
Method and device for power predication of wind power station Download PDFInfo
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Abstract
The invention provides a method and a device for power predication of a wind power station. The method for power predication of the wind power station includes: acquiring weather forecast data outputted by a numerical weather forecast system; using CFD (computational fluid dynamics) software for optimization calculation of the weather forecast data to obtain weather predication data of the wind power station; using a preset corresponding relation between an output power and weather data in a statistic model to obtain power predication data of the wind power station according to the weather predication data; and outputting the power predication data. Using the method and the device for power predication of the wind power station realizes data coupling of the numerical weather forecast system and a CFD system and obtains more precise point positions of fans and weather information of wind speed, wind direction and the like at the heights of the fans, the power predication data are obtained by means of the statistic model according to the weather predication data, inaccuracy caused by a power curve iterative computation mode is avoided, difference of power curves of different fans is eliminated, and accordingly precision in power predication of the wind power station is improved.
Description
Technical field
The present invention relates to new energy field, in particular to a kind of wind energy turbine set power forecasting method and prediction unit.
Background technology
The shared proportion in electric system of wind power generation is with annual 30% speed increment.But because wind energy has undulatory property, intermittence, low energy densities, characteristics such as uncontrollable, thus wind power also be fluctuation, intermittently.Cause jumbo wind-powered electricity generation to insert electrical network and will bring a little new problems safety, the stable operation of electric system, especially obvious to the influence of Operation of Electric Systems scheduling.Therefore, exploitation and use wind energy turbine set power forecasting method are significant.
The wind energy turbine set power prediction is a very complicated problems; Relate to relevant various interdisciplines, like meteorology, fluid dynamics; Subjects such as stochastic process; Wherein the numerical weather forecast in the wind energy turbine set is the challenging problem of tool, and particularly in area with a varied topography and the area that has pollutant to exist, the change in time and space of wind speed is more rapid.The wind energy turbine set power forecasting method that uses at present mainly contains:
1, numerical weather forecast technology and statistical method are utilized the California of forecasting techniques first Application during 1985-1987 of the combination of numerical weather forecast technology and statistical method.European Center for Medium Weather Forecasting (ECMWF) also develops a kind of like this numerical forecasting system in Britain; Utilize the statistical correction technology to combine 15 kilometers horizontal resolution grid numerical forecasting cooperations; This techniques make use statistical model alignment technique is further optimized the result of numerical weather forecast; And dimensionality reduction is on the physical location of wind energy turbine set, but its precision far can not reach the requirement of wind energy turbine set power prediction.
2, use the numerical weather forecast technology and the WASP technology of low resolution, the HIRLAM/WASP system is responsible for developing by Denmark weather bureau and National Laboratory.It mainly is included in HIRLAM and goes up the numerical forecasting of 56 kilometers horizontal resolution grids of operation and use WASP to be used for estimating the wake effect of wind energy turbine set regional area.At Crete, the HIRLAM/WASP application of model has obtained gratifying predicting the outcome, but its shortcoming is to embody the influence of complex-terrain.
3, high-resolution numerical weather forecast technology; The Previento prognoses system is developed out in Oldenburg university; It is based on a high-resolution meso-scale model; And consider the influence (like roughness etc.) of local landform, also considered the factors such as heat stability of atmosphere, thereby estimated wind speed better at the wind generator unit wheel hub height.But the physical model of not setting up and analyze microscale is also reasonably used.This system mainly operates in Germany.
Through the wind-force data that above numerical weather forecast technology draws, draw the output power predicted value of blower fan through the powertrace iterative computation.
The not high problem of wind energy turbine set power forecasting method precision of prediction to existing in the prior art does not still propose effective solution at present.
Summary of the invention
The present invention aims to provide a kind of wind energy turbine set power forecasting method and prediction unit, to solve the not high problem of wind energy turbine set power forecasting method precision of prediction in the prior art.
To achieve these goals, according to an aspect of the present invention, a kind of wind energy turbine set power forecasting method is provided.This wind energy turbine set power forecasting method comprises: the data of weather forecast that obtains the output of numerical weather forecast system; Utilize CFD software that data of weather forecast is optimized calculating to obtain the weather prognosis data of wind energy turbine set; The corresponding relation of weather data and output power draws the power prediction data of wind energy turbine set in the statistical model that use is set up in advance according to the weather prognosis data; The output power predicted data.
Further, the data of weather forecast that obtains numerical weather forecast system output comprises: the numerical weather forecast data of obtaining the highest level grid precision of COAMPS system.
Further, utilizing CFD software that data of weather forecast is optimized calculating comprises with the weather prognosis data that obtain wind energy turbine set: utilize the CFD software logarithm value data of weather forecast that is provided with wind energy turbine set actual environment parameter to be optimized and calculate meteorological tentative prediction data; Meteorological tentative prediction data are carried out Kalman filtering to be calculated to obtain the weather prognosis data of wind energy turbine set.
Further, meteorological tentative prediction data being carried out Kalman filtering calculates also to comprise after the weather prognosis data that obtain wind energy turbine set: the weather prognosis data of wind energy turbine set are sent to the numerical weather forecast system.
Further, the corresponding relation of weather data and output power comprises according to the power prediction data that the weather prognosis data draw wind energy turbine set in the statistical model of use foundation in advance: with weather prognosis data input neural network model; Utilize neural network model to calculate to draw the preliminary power predicted data; The preliminary power predicted data is carried out the power prediction data that Kalman filtering calculates wind energy turbine set, and wherein, neural network model is to utilize the weather data of wind energy turbine set and the statistical model that the output power data are set up in advance.
Further, the output power predicted data comprises: use client-server mode or browser server mode to export the power prediction value of optimization.
Further, the output power predicted data comprises: with power prediction data upload to dispatching of power netwoks server.
According to a further aspect in the invention, a kind of wind energy turbine set powder prediction device is provided.This wind energy turbine set powder prediction device comprises: the weather forecast acquisition module is used to obtain the data of weather forecast of numerical weather forecast system output; The weather prognosis module is used to utilize CFD software that data of weather forecast is optimized calculating to obtain the weather prognosis data of wind energy turbine set; The power prediction module is used for using in advance the corresponding relation of the statistical model weather data set up and output power to draw the power prediction data of wind energy turbine set according to the weather prognosis data; Output module is used for the output power predicted data.
Further, the weather prognosis module also is used for: utilize the CFD software logarithm value data of weather forecast that is provided with wind energy turbine set actual environment parameter to be optimized and calculate meteorological tentative prediction data; Meteorological tentative prediction data are carried out Kalman filtering to be calculated to obtain the weather prognosis data of wind energy turbine set.
Further, the power prediction module also is used for: with weather prognosis data input neural network model; Utilize neural network model to calculate to draw the preliminary power predicted data; The preliminary power predicted data is carried out the power prediction data that Kalman filtering calculates wind energy turbine set, and wherein, neural network model is to utilize the weather data of wind energy turbine set and the statistical model that the output power data are set up in advance.
Use technical scheme of the present invention, the wind energy turbine set power forecasting method comprises: the data of weather forecast that obtains the output of numerical weather forecast system; Utilize CFD software that data of weather forecast is optimized calculating to obtain the weather prognosis data of wind energy turbine set; The corresponding relation of weather data and output power draws the power prediction data of wind energy turbine set in the statistical model that use is set up in advance according to the weather prognosis data; The output power predicted data.Use the wind energy turbine set powder prediction device of the embodiment of the invention to realize that the data of infusion weather forecast system and CFD system are coupled; Draw the weather informations such as wind speed and direction of accurate more blower fan loca position and blower fan place height; And use the method for statistical model to draw the power prediction data by the weather prognosis data; The inaccuracy of having avoided powertrace iterative computation mode to cause; And eliminated the otherness of powertrace between each blower fan, thus improved the accuracy of wind energy turbine set power prediction, for the electric energy scheduling level that further improves electrical network provides the foundation.
Description of drawings
The Figure of description that constitutes the application's a part is used to provide further understanding of the present invention, and illustrative examples of the present invention and explanation thereof are used to explain the present invention, do not constitute improper qualification of the present invention.In the accompanying drawings:
Fig. 1 is the wind energy turbine set powder prediction device according to the embodiment of the invention
Fig. 2 is the synoptic diagram according to the COAMPS system nested grid of the wind energy turbine set powder prediction device of the embodiment of the invention;
Fig. 3 is the synoptic diagram according to the wind energy turbine set power forecasting method of the embodiment of the invention.
Embodiment
Need to prove that under the situation of not conflicting, embodiment and the characteristic among the embodiment among the application can make up each other.Below with reference to accompanying drawing and combine embodiment to specify the present invention.
Fig. 1 is the wind energy turbine set powder prediction device according to the embodiment of the invention, and is as shown in Figure 1, and the wind energy turbine set powder prediction device of the embodiment of the invention comprises: weather forecast acquisition module 11 is used to obtain the data of weather forecast of numerical weather forecast system output; Weather prognosis module 13 is used to utilize CFD software that data of weather forecast is optimized calculating to obtain the weather prognosis data of wind energy turbine set; Power prediction module 15 is used for using in advance the corresponding relation of the statistical model weather data set up and output power to draw the power prediction data of wind energy turbine set according to the weather prognosis data; Output module 17 is used for the output power predicted data.
Use the wind energy turbine set powder prediction device of the embodiment of the invention to realize that the data of numerical weather forecast system and CFD system are coupled; Draw the weather informations such as wind speed and direction of accurate more blower fan loca position and blower fan place height; And use the method for statistical model to draw the power prediction data by the weather prognosis data; The inaccuracy of having avoided powertrace iterative computation mode to cause; And eliminated the otherness of powertrace between each blower fan, thus improved the accuracy of wind energy turbine set power prediction, for the electric energy scheduling level that further improves electrical network provides the foundation.
Wherein, weather forecast acquisition module 11 concrete steps of obtaining the data of weather forecast of numerical weather forecast system output can be the numerical weather forecast data of the highest level grid precision of obtaining the COAMPS system.COAMPS (Coupled Ocean/Atmosphere Mesoscale Prediction System; Coupling ocean/atmosphere mesoscale forecast system) be the mesoscale numerical weather forecast system of the ocean/atmosphere that has been coupled, it is developed by United States Naval Research Laboratory.The COAMPS pattern is a non-static(al) model of three-dimensional, can carry out the numerical weather forecast computing to zone large-scale and with a varied topography.The execution time of numerical forecasting module is decided by the nested number of plies.The nested grid that uses varying number with its carelessness of Atmospheric models of realizing a given high-resolution region as a result precision also have than big-difference.Fig. 2 is the synoptic diagram according to the COAMPS system nested grid of the wind energy turbine set powder prediction device of the embodiment of the invention, and is as shown in Figure 2, and the forecast sizing grid of COAMPS can change in about 100 meters from the hundreds of kilometer.
CFD in the weather prognosis module 13 (Computational Fluid Dynamics; Computational fluid dynamics) software; Can carry out the microcosmic prediction to wind energy turbine set; Existing common CFD software comprises: FLUENT, Phoenics, CFX, Star-cd etc.; The data of weather forecast of numerical weather forecast system (like COAMPS) output is as the input of the wind energy turbine set interior microscopic CFD of system software, and the boundary condition (like wind profile, the vertical distribution of Turbulent Kinetic etc.) when problem such as computation bound can be provided.CFD computed in software parameter can be provided with according to the actual environmental parameter of wind energy turbine set, and the aforementioned calculation parameter comprises turbulent parameters, roughness and the heat stability of wind energy turbine set.
Utilize in the weather prognosis module 13 CFD software to data of weather forecast be optimized calculating with the concrete steps of the weather prognosis data that obtain wind energy turbine set can for: utilize the CFD software logarithm value data of weather forecast that is provided with wind energy turbine set actual environment parameter to be optimized and calculate meteorological tentative prediction data; Meteorological tentative prediction data are carried out Kalman filtering to be calculated to obtain the weather prognosis data of wind energy turbine set.Calculate through Kalman filtering, can combine the weather data of actual acquisition that the weather prognosis data are carried out further precision optimizing, thereby make weather prognosis more accurate.
Weather datas such as the wind speed that more than calculates through Kalman filtering, wind direction, temperature can also be sent to the numerical weather forecast system, think that the numerical weather forecast of following one-period provides the basis.
Power prediction module 15 can also be used for: with above-mentioned weather prognosis data input neural network model; Utilize neural network model to calculate to draw the preliminary power predicted data; The preliminary power predicted data is carried out the power prediction data that Kalman filtering calculates wind energy turbine set, and wherein, neural network model is to utilize the weather data of wind energy turbine set and the statistical model that the output power data are set up in advance.This statistical model generates neural network model as the basis according to neural network algorithm according to the historical data of wind energy turbine set non-volatile recording, in this model, has set up the corresponding relation between wind speed variable and the power and variable.When using this statistical model; Can be with above-mentioned weather prognosis data input neural network model; Carry out the preliminary power predicted data of output wind energy turbine set after the computing by neural network model; Combine the actual value of measurement of last one-period to carry out to draw after Kalman filtering is calculated the power prediction data of wind energy turbine set to this preliminary power predicted data, further improved precision of prediction.
The mode of output module 17 output power predicted data can be to use client-server mode or browser server mode to export the power prediction value of optimization, thereby lets the data consumer utilize client or browser directly to obtain above data.
The another kind of mode of output module 17 output power predicted data is that the output power predicted data comprises: with power prediction data upload to dispatching of power netwoks server, the dispatcher utilizes this predicted data to carry out the scheduling of electric energy.
The embodiment of the invention also provides a kind of wind energy turbine set power forecasting method; This wind energy turbine set power forecasting method can be carried out through any wind energy turbine set powder prediction device that the above embodiment of the present invention provided; Fig. 3 is the synoptic diagram according to the wind energy turbine set power forecasting method of the embodiment of the invention; As shown in Figure 3, this wind energy turbine set power forecasting method comprises:
Step S31 obtains the data of weather forecast of numerical weather forecast system output;
Step S33 utilizes CFD software that data of weather forecast is optimized calculating to obtain the weather prognosis data of wind energy turbine set;
Step S35, the corresponding relation of weather data and output power draws the power prediction data of wind energy turbine set in the statistical model that use is set up in advance according to the weather prognosis data;
Step S37, the output power predicted data.
Wherein, step S31 can comprise the numerical weather forecast data of the highest level grid precision of obtaining the COAMPS system.Utilize the COAMPS numerical weather forecast of high grid precision can be except that obtaining comprising the accurate weather forecast of complex-terrains such as land, ocean; Can also obtain the air pollution situation, comprise influence to the wind power forecast precision such as sandstorm (comprising sandstorm etc.) thereby can obtain the air pollution situation.
Step S33 can comprise: utilize the CFD software logarithm value data of weather forecast that is provided with wind energy turbine set actual environment parameter to be optimized and calculate meteorological tentative prediction data; Meteorological tentative prediction data are carried out Kalman filtering to be calculated to obtain the weather prognosis data of wind energy turbine set.Thereby, obtain the wind speed and direction of accurate blower fan place current potential and height through the coupling of bidirectional traffic interaction mode realization numerical weather forecast and wind energy turbine set microcosmic prognoses system.Wherein, with the output of COAMPS input, necessary boundary condition (comprising wind profile, the vertical distribution of Turbulent Kinetic) is provided during for problems such as its computation bounds as the wind energy turbine set interior microscopic CFD of system model.
Also comprise after calculating with the weather prognosis data that obtain wind energy turbine set meteorological tentative prediction data being carried out Kalman filtering: the weather prognosis data of wind energy turbine set are sent to the numerical weather forecast system; With the input of the weather informations such as wind speed, wind direction and temperature after the CFD software optimization, make that the output of next cycle COAMPS is more accurate as COAMPS.
Step S35 can comprise: with weather prognosis data input neural network model; Utilize neural network model to calculate to draw the preliminary power predicted data; The preliminary power predicted data is carried out the power prediction data that Kalman filtering calculates wind energy turbine set, and wherein, neural network model is to utilize the weather data of wind energy turbine set and the statistical model that the output power data are set up in advance.
Above-mentioned twice Kalman filtering to weather information data and power prediction data calculated, based on actual acquisition to weather data and output power information predicted value has been carried out feedback correction, further improved the accuracy of predicted data.
Step S37 can comprise and uses client-server mode or browser server mode to export the power prediction value of optimization, perhaps with power prediction data upload to dispatching of power netwoks server.For the further ADVANCED APPLICATIONS of data provides interface.
From above description; Can find out that the above embodiments of the present invention have realized following technique effect: realized the data coupling of infusion weather forecast system and CFD system; Draw the weather informations such as wind speed and direction of accurate more blower fan loca position and blower fan place height; And use the method for statistical model to draw the power prediction data by the weather prognosis data, the inaccuracy of having avoided powertrace iterative computation mode to cause, and eliminated the otherness of powertrace between each blower fan; Thereby improved the accuracy of wind energy turbine set power prediction, for the electric energy scheduling level that further improves electrical network provides the foundation.
Obviously, it is apparent to those skilled in the art that above-mentioned each module of the present invention or each step can realize with the general calculation device; They can concentrate on the single calculation element; Perhaps be distributed on the network that a plurality of calculation element forms, alternatively, they can be realized with the executable program code of calculation element; Thereby; Can they be stored in the memory storage and carry out, perhaps they are made into each integrated circuit modules respectively, perhaps a plurality of modules in them or step are made into the single integrated circuit module and realize by calculation element.Like this, the present invention is not restricted to any specific hardware and software combination.
The above is merely the preferred embodiments of the present invention, is not limited to the present invention, and for a person skilled in the art, the present invention can have various changes and variation.All within spirit of the present invention and principle, any modification of being done, be equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (10)
1. a wind energy turbine set power forecasting method is characterized in that, comprising:
Obtain the data of weather forecast of numerical weather forecast system output;
Utilize CFD software that said data of weather forecast is optimized calculating to obtain the weather prognosis data of wind energy turbine set;
The corresponding relation of weather data and output power draws the power prediction data of wind energy turbine set in the statistical model that use is set up in advance according to said weather prognosis data;
Export said power prediction data.
2. Forecasting Methodology according to claim 1 is characterized in that, the data of weather forecast that obtains the output of numerical weather forecast system comprises: the numerical weather forecast data of obtaining the highest level grid precision of COAMPS system.
3. Forecasting Methodology according to claim 1 and 2 is characterized in that, utilizes CFD software that said data of weather forecast is optimized calculating and comprises with the weather prognosis data that obtain wind energy turbine set:
The CFD software that utilization is provided with said wind energy turbine set actual environment parameter is optimized said numerical weather forecast data and calculates meteorological tentative prediction data;
Said meteorological tentative prediction data are carried out Kalman filtering to be calculated to obtain the weather prognosis data of said wind energy turbine set.
4. Forecasting Methodology according to claim 3 is characterized in that, said meteorological tentative prediction data is carried out Kalman filtering calculate also to comprise after the weather prognosis data that obtain said wind energy turbine set:
The weather prognosis data of said wind energy turbine set are sent to said numerical weather forecast system.
5. according to each described Forecasting Methodology in the claim 1 to 4, it is characterized in that the corresponding relation of weather data and output power comprises according to the power prediction data that said weather prognosis data draw wind energy turbine set in the statistical model that use is set up in advance:
With said weather prognosis data input neural network model;
Utilize said neural network model to calculate to draw the preliminary power predicted data;
Said preliminary power predicted data is carried out the power prediction data that Kalman filtering calculates wind energy turbine set, and wherein, said neural network model is to utilize the weather data of wind energy turbine set and the statistical model that the output power data are set up in advance.
6. according to each described Forecasting Methodology in the claim 1 to 4, it is characterized in that, export said power prediction data and comprise: use client-server mode or browser server mode to export the power prediction value of said optimization.
7. according to each described Forecasting Methodology in the claim 1 to 4, it is characterized in that, export said power prediction data and comprise: with said power prediction data upload to the dispatching of power netwoks server.
8. a wind energy turbine set powder prediction device is characterized in that, comprising:
The weather forecast acquisition module is used to obtain the data of weather forecast of numerical weather forecast system output;
The weather prognosis module is used to utilize CFD software that said data of weather forecast is optimized calculating to obtain the weather prognosis data of wind energy turbine set;
The power prediction module is used for using in advance the corresponding relation of the statistical model weather data set up and output power to draw the power prediction data of wind energy turbine set according to said weather prognosis data;
Output module is used to export said power prediction data.
9. prediction unit according to claim 8 is characterized in that, said weather prognosis module also is used for:
The CFD software that utilization is provided with said wind energy turbine set actual environment parameter is optimized said numerical weather forecast data and calculates meteorological tentative prediction data;
Said meteorological tentative prediction data are carried out Kalman filtering to be calculated to obtain the weather prognosis data of said wind energy turbine set.
10. according to Claim 8 or 9 described prediction units, it is characterized in that said power prediction module also is used for:
With said weather prognosis data input neural network model;
Utilize said neural network model to calculate to draw the preliminary power predicted data
Said preliminary power predicted data is carried out the power prediction data that Kalman filtering calculates wind energy turbine set,
Wherein, said neural network model is to utilize the weather data of wind energy turbine set and the statistical model that the output power data are set up in advance.
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| CN103617454A (en) * | 2013-11-21 | 2014-03-05 | 中能电力科技开发有限公司 | Wind power plant power forecast method according to numerical weather forecasts |
| CN107255978A (en) * | 2017-05-27 | 2017-10-17 | 中国大唐集团科学技术研究院有限公司 | Method for visually monitoring, apparatus and system based on CFD flow field analyses |
| CN108629056A (en) * | 2017-03-17 | 2018-10-09 | 中国电力科学研究院 | A kind of wind power forecasting method and system |
| CN105320809B (en) * | 2015-09-30 | 2019-01-25 | 西安电子科技大学 | A kind of wind speed forecasting method for wind power plant spatial coherence |
| CN110009037A (en) * | 2019-04-03 | 2019-07-12 | 中南大学 | A kind of engineering wind speed Forecasting Approach for Short-term and system based on physical message coupling |
| CN111340307A (en) * | 2020-03-13 | 2020-06-26 | 华润电力技术研究院有限公司 | Method for predicting wind power generation power of fan and related device |
| CN112819218A (en) * | 2021-01-27 | 2021-05-18 | 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) | High-resolution urban mobile source pollution space-time prediction method, system and storage medium |
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Cited By (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103617454A (en) * | 2013-11-21 | 2014-03-05 | 中能电力科技开发有限公司 | Wind power plant power forecast method according to numerical weather forecasts |
| CN105320809B (en) * | 2015-09-30 | 2019-01-25 | 西安电子科技大学 | A kind of wind speed forecasting method for wind power plant spatial coherence |
| CN108629056A (en) * | 2017-03-17 | 2018-10-09 | 中国电力科学研究院 | A kind of wind power forecasting method and system |
| CN107255978A (en) * | 2017-05-27 | 2017-10-17 | 中国大唐集团科学技术研究院有限公司 | Method for visually monitoring, apparatus and system based on CFD flow field analyses |
| CN107255978B (en) * | 2017-05-27 | 2019-11-08 | 中国大唐集团科学技术研究院有限公司 | Method for visually monitoring, apparatus and system based on CFD flow field analysis |
| CN110009037A (en) * | 2019-04-03 | 2019-07-12 | 中南大学 | A kind of engineering wind speed Forecasting Approach for Short-term and system based on physical message coupling |
| CN110009037B (en) * | 2019-04-03 | 2020-10-27 | 中南大学 | Short-term engineering wind speed prediction method and system based on physical information coupling |
| CN111340307A (en) * | 2020-03-13 | 2020-06-26 | 华润电力技术研究院有限公司 | Method for predicting wind power generation power of fan and related device |
| CN111340307B (en) * | 2020-03-13 | 2023-08-04 | 华润电力技术研究院有限公司 | Method for predicting wind power generation power of fan and related device |
| CN112819218A (en) * | 2021-01-27 | 2021-05-18 | 合肥综合性国家科学中心人工智能研究院(安徽省人工智能实验室) | High-resolution urban mobile source pollution space-time prediction method, system and storage medium |
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Application publication date: 20121107 |