CN101794996A - Real-time predicting method for output of wind electric field - Google Patents
Real-time predicting method for output of wind electric field Download PDFInfo
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Abstract
The invention relates to a real-time predicting method for output of a wind electric field, which aims at providing a real-time predicting method for output of a wind electric field, which has high predicting precision, strong practicality and short predicting reaction time. The output of the wind electric field is predicted through predicting the wind speed of the wind electric field, and the invention aims to provide reliable basis for a power network to make a schedule plan. The technical problem for solving the problem is as follows: the real-time predicting method for output of a wind electric field is characterized by comprising the following steps of: a. simulating a large-scale circulation field and a meteorological field by a number weather reporting mode according to the real-time refreshed meteorological grid point initial data, and deducing the wind resource state of the representative point; b. sending the wind resource state obtained from the step a to an output calculating model; c. deducing the output of the whole wind electric field by using the output calculating model, and furthermore, deducing the output of a wind electric field group. The invention is mainly used in the wind energy utilizing field, predicts the electric generation amount of a large wind electric generating field, and provides basis for the power network schedule.
Description
Technical field
The present invention relates to a kind of real-time predicting method for output of wind electric field.Mainly be applicable to field of wind energy utilization, the prediction of large-scale wind power field energy output is for dispatching of power netwoks provides foundation.
Background technology
To be the wind-driven generator that utilizes electromechanical integration be converted into a kind of generation mode of electric energy with the kinetic energy of wind to wind-powered electricity generation, and the size of exerting oneself depends primarily on the size of wind speed, the variation tendency of its process of exerting oneself and change of wind velocity basically identical.The forming process of wind is complicated, mainly is subjected to the influence of solar radiation and earth rotation, has stronger randomness.The exerting oneself of wind power generation of characteristic decision of wind also has certain randomness, and the scheduling and the safe operation that contain the electrical network of certain wind-powered electricity generation are brand-new problems.Along with the fast development of " three Norths " regional wind-powered electricity generation, Gansu and Inner Mongol are for the safe and stable operation of electrical network, and the wind-powered electricity generation that winter, frequent restriction was connected to the grid is exerted oneself, and the wind energy turbine set electricity volume is reduced, and the income of wind-powered electricity generation manufacturing enterprise reduces, even loss occurs.Realize that the wind-powered electricity generation electric weight fully purchased by electrical network, need carry out many-sided work.Understanding the characteristics that wind-powered electricity generation is exerted oneself, predict that successfully wind-powered electricity generation exerts oneself, formulate operation plan for electrical network reliable foundation is provided, is a very important job.
Because constantly increase is dropped in the construction of wind energy turbine set in Europe in recent years, and big quantity research has been carried out in the prediction of wind energy and wind energy turbine set, developed the system that several are used for the prediction of wind energy turbine set energy output, and practice is in a plurality of wind energy turbine set.Because these systems are difficult for purchasing, and versatility is not strong, be difficult for predicted results being adjusted according to the characteristics of forecasting object, therefore, the output of wind electric field prognoses system that research and development are suitable for the native country is very necessary.
Common forecasting method can be divided into two kinds of mathematical statistical model, numerical weather prediction models.China's wind power technology is started late, and does not also have the ripe prognoses system of exerting oneself to come out at present.The domestic existing research mathematical statistical model that adopt more, the core of its prediction is a statistical principle, is based upon on the basis of a large amount of historical datas, and the influence that surrounding enviroment change forecasting object can't embody, and predicated error is bigger, and the error of predicting wind speed of wind farm is about 25～40%.
Summary of the invention
The technical problem to be solved in the present invention is: provide a kind of precision of prediction height, practical, real-time predicting method for output of wind electric field that the prediction reaction time is short at the problem of above-mentioned existence, predict exerting oneself of wind energy turbine set by the prediction wind farm wind velocity, being intended to formulate operation plan for electrical network provides reliable foundation.
The technical solution adopted in the present invention is: real-time predicting method for output of wind electric field is characterized in that may further comprise the steps:
A, according to the initial data of meteorological lattice point of real-time update, utilize numerical weather prediction model simulation large-scale circulation field, meteorological field, the wind-resources situation of inquiring into representative point;
B, the wind-resources situation that step a is obtained transfer to the computation model of exerting oneself;
C, utilize the computation model of exerting oneself to calculate whole output of wind electric field, and then calculate exerting oneself of wind farm group.
Described step a also comprises,
A1, utilize the wind-resources situation of measured data checking representative point, judge whether to satisfy required precision, if satisfy then execution in step b; If do not satisfy, then execution in step a2;
A2, adjustment modes resolution and physical parameter scheme recomputate.
Described step c comprises,
C1, set up wind energy turbine set WAsP energy output computation model,, calculate all quadrants sampling factor of correspondence by the relation that wind direction divides quadrant to analyze representative point and whole output of wind electric field;
C2, based on representative point wind regime and wind-powered electricity generation power of the assembling unit curve, calculation representative point is exerted oneself;
C3, exert oneself and all quadrants sampling factor, calculate output of wind electric field, and then calculate exerting oneself of wind farm group according to representative point.
When carrying out the branch quadrant among the above-mentioned steps c1, being divided into by the wind direction difference is 16 quadrants, i.e. N, NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, WSW, W, WNW, NW, NNW.
Described step c also comprises,
C4, utilize actual operating data checking wind farm group to exert oneself, if do not satisfy required precision, then execution in step c5;
C5, adjust the computation model of exerting oneself, recomputate.
The invention has the beneficial effects as follows: 1, the present invention is applied to predicting wind speed of wind farm with numerical weather prediction model first.Numerical weather prediction model is set about from the angle of atmospheric circulation, the wind-resources situation in the research range predicted, and can be according to the variation adjustment modes of surrounding enviroment, precision of prediction is higher, and is practical.2, adopt branch quadrant sampling factor to calculate output of wind electric field.On the basis of the energy output computation model (WAsP) when wind energy turbine set designs, by the representative point that wind-resources real-time estimate result is arranged, by wind direction divide each wind speed of quadrant statistics representative point exert oneself and the exerting oneself of wind energy turbine set between relation, be defined as all quadrants sampling factor, utilize sampling factor to calculate single wind energy turbine set, exerting oneself in real time of wind farm group, this method has avoided utilizing the huge amount of calculation of fluid mechanic model, the efficient of work and the reaction time of prediction have been improved, also taken into account of the influence of boundary condition difference by the different quadrant statistics of wind direction sampling factor, improved precision of prediction result of calculation.3, on the whole can the better prediction wind farm wind velocity and exert oneself, do not need long-term observation data and actual motion data, and its prediction result is more accurate for the assurance of main trend, the forecasting wind speed error is about 10%, and the predicated error of exerting oneself is about 22%.
Description of drawings
Fig. 1 is a system block diagram of the present invention.
Fig. 2 is a numerical weather prediction model block diagram among the present invention.
Fig. 3 is the computation model block diagram of exerting oneself among the present invention.
Embodiment
As shown in Figure 1, present embodiment mainly comprise numerical weather prediction model, the computation model two parts of exerting oneself.This numerical weather prediction model of selecting for use is WRF (Weather Research Forecast) pattern.The WRF modular system is mesoscale Forecast Mode of new generation and the assimilation system that the meteorological boundary of the U.S. develops jointly, emphasis is considered the forecast from the cloud yardstick to significant weathers such as synoptic scales, the horizontal resolution emphasis is considered the simulation and the forecast of 1～10km lattice distance, as a commonality schemata, the terrain data of its source program, multiple resolution type and the free externally issue of in real time initial data.According to the initial data of meteorological lattice point that timing advance is openly issued, utilization numerical weather prediction model (WRF) prediction wind farm wind velocity.Designing simultaneously with minute quadrant sampling factor is the computation model of exerting oneself of core, is bridge with representative observation point wind-resources situation, and exerting oneself of wind energy turbine set predicted.Can also increase the prediction of exerting oneself according to the construction situation of estimation range wind energy turbine set to new operation wind energy turbine set.
As shown in Figure 2, before numerical weather prediction model (WRF) is predicted, need clear and definite simulated domain, selected terrain data type according to repeatedly controlled test, determines to be applicable to the model resolution and the physical schemes parameter of one's respective area simulation.For example the jiangsu coast wind energy turbine set is given the correct time in advance, the pattern horizontal resolution of setting is 5km * 5km, and it is 90 * 110 that HORIZONTAL PLAID is counted, 33.0 ° of N of simulated domain central point, 120.5 ° of E, and level is to containing whole jiangsu coast zone; Mode layer top air pressure P is set
t=50hPa, 28 layers of pattern vertical demixings, and below the 100m height, encrypt, each layer σ value is chosen for 1.000,0.996,0.990 respectively, 0.985,0.978,0.960,0.946,0.922,0.894,0.860,0.817,0.766,0.707,0.664,0.576,0.507,0.444,0.380,0.324,0.273,0.188,0.152,0.121,0.093,0.069,0.048,0.020,0.000; The pattern step-length time of integration is got 30s; The main physical parameters scheme sees the following form.
WRF pattern Main physical scheme
| Project | The employing scheme |
| The speck reason | The WSM3 class is simply iced scheme |
| Long-wave radiation | The rrtm scheme |
| Shortwave radiation | The Dudhia scheme |
| Ground layer | The Monin-Obukhov scheme |
| The land face | The heat diffusion scheme |
| The boundary layer | The YSU scheme |
| Cumulus convection | Shallow convection Kain-Fritsch (new Eta) scheme |
Step is as follows during prediction:
The initial data of meteorological lattice point that step S1 openly issues according to topographic(al) data and timing advance is carried out initialization process to mode state;
Model resolution and physical schemes parameter that step S2 determines according to controlled test utilize WRF mode computation main program to carry out mode computation;
Step S3 carries out visualization processing to the mode computation result;
Step S4 obtains large-scale circulation field and various meteorological element field;
Step S5 inquires into representative point wind-resources situation;
Step S6 utilizes wind-resources actual observation data, and the result compares checking to output;
Does step S7 judge that the output result satisfies required precision? if satisfy required precision, then go to step S8; If do not satisfy required precision, then adjustment modes resolution or physical schemes parameter once more, and go to step S2 and carry out analog computation again;
Step S8 output representative point hub height wind speed and direction is to the computation model of exerting oneself.
As shown in Figure 3, employing is exerted oneself before computation model predicts, set up the WAsP computation model with the survey wind data at least one year of anemometer tower in the wind energy turbine set, topographic(al) data, wind-powered electricity generation machine unit characteristic data, according to the complete 1 year actual motion data of this wind energy turbine set, wind energy turbine set WAsP computation model boundary condition and parameter are adjusted.Characteristics according to wind energy turbine set topography and geomorphology, blower fan layout, select certain typhoon machine as representative point, according to adjusted wind energy turbine set WAsP energy output computation model, divide 16 quadrants again by wind direction, set up the relation of representative point and whole output of wind electric field, calculate corresponding all quadrants sampling factor.Step is as follows during prediction:
Step S11 is based on the representative point hub height wind speed and direction of wind-powered electricity generation power of the assembling unit curve and numerical weather prediction model simulation, and calculation representative point is exerted oneself;
Step S21 determines its place quadrant according to the representative point wind direction, and in conjunction with the sampling factor of this quadrant, calculates exerting oneself of whole wind electric field;
Step S31 obtains exerting oneself of wind farm group with the stack of exerting oneself of each wind energy turbine set;
Step S41 utilizes the wind energy turbine set actual operating data to compare checking;
Step S51 judges that whether the output result satisfies required precision, if satisfy required precision, then goes to step S61; Adjust the computation model of exerting oneself (mainly being the sampling factor of adjusting all quadrants) otherwise return, calculate again;
The step S61 prediction of output is exerted oneself.
In the above-mentioned steps, difference according to the installed capacity of wind energy turbine set, in each wind energy turbine set, set one or more representative points, wind-resources situation and wind-powered electricity generation power of the assembling unit curve based on each representative point, calculate exerting oneself of each representative point, exerting oneself of each wind energy turbine set is the sum of exerting oneself of all representative points in this wind energy turbine set, and all output of wind electric field sums are exerting oneself for whole wind farm group then.
In this example, all quadrants sampling factor computational methods are as follows: wind energy turbine set WAsP computation model calculates exerting oneself of the interior every typhoon machine of wind energy turbine set, simultaneously with reference to the actual motion data at least one year of wind energy turbine set, select wherein representative blower fan as representative point, the ratio of exerting oneself of the exerting oneself of whole wind electric field (i.e. all blower fans exert oneself sum) and this representative point, be panoramic limit sampling factor, divide 16 quadrants by wind direction, the ratio of exerting oneself with this representative point of exerting oneself of whole wind electric field then is the sampling factor of all quadrants in each quadrant.The selected blower fan that is numbered m is a representative point, i quadrant m blower fan to wind energy turbine set sampling factor ki can be calculated as follows.
Wherein:
P refers to exerting oneself of blower fan;
Q refers to fan capacity;
N refers to the total platform number of wind electric field blower;
J refers to the blower fan numbering, is 1 ... n;
M refers to the blower fan numbering of selected representative point;
I quadrant numbering is 1 ... 16.
Present embodiment can carry out according to following steps in actual mechanical process:
1, determines simulated domain, collect terrain data, utilize numerical weather prediction model (WRF) to carry out controlled test, determine to be applicable to the model resolution and the physical schemes parameter of one's respective area simulation;
2, set up the WAsP computation model with the survey wind data at least one year of anemometer tower in the wind energy turbine set, topographic(al) data, wind-powered electricity generation machine unit characteristic data, according to the complete 1 year actual motion data of this wind energy turbine set, wind energy turbine set WAsP computation model boundary condition and parameter are adjusted, set up the relation of representative point and whole output of wind electric field, calculate corresponding sampling factor;
3,, utilize numerical weather prediction model (WRF) simulation large-scale circulation field, meteorological field, the wind-resources situation of inquiring into representative point according to the initial data of meteorological lattice point (timing advance is openly issued) of real-time update;
4, utilize the on-the-spot actual observation data of wind-resources, checking and adjustment WRF pattern are perhaps adjusted the WRF pattern according to the variation of surrounding enviroment, finally draw the representative point hub height wind speed and direction that satisfies required precision;
5, based on representative point wind regime and wind-powered electricity generation power of the assembling unit curve, calculation representative point is exerted oneself, and in conjunction with all quadrants sampling factor, calculates whole output of wind electric field, and then calculates exerting oneself of wind farm group;
6, utilize the wind energy turbine set actual operating data to compare checking, adjust the computation model of exerting oneself;
7, each wind energy turbine set is predicted the outcome, information such as comprise the wind energy turbine set meteorology, exert oneself is gathered, is transferred to the prediction control centre and integrates, and re-sends to each user terminal.
Claims (5)
1. real-time predicting method for output of wind electric field is characterized in that may further comprise the steps:
A, according to the initial data of meteorological lattice point of real-time update, utilize numerical weather prediction model simulation large-scale circulation field, meteorological field, the wind-resources situation of inquiring into representative point,
B, the wind-resources situation that step a is obtained transfer to the computation model of exerting oneself;
C, utilize the computation model of exerting oneself to calculate whole output of wind electric field, and then calculate exerting oneself of wind farm group.
2. real-time predicting method for output of wind electric field according to claim 1 is characterized in that: described step a also comprises,
A1, utilize the wind-resources situation of measured data checking representative point, judge whether to satisfy required precision, if satisfy then execution in step b; If do not satisfy, then execution in step a2;
A2, adjustment modes resolution and physical parameter scheme recomputate.
3. real-time predicting method for output of wind electric field according to claim 1 is characterized in that: described step c comprises,
C1, set up wind energy turbine set WAsP energy output computation model,, calculate all quadrants sampling factor of correspondence by the relation that wind direction divides quadrant to analyze representative point and whole output of wind electric field;
C2, based on representative point wind regime and wind-powered electricity generation power of the assembling unit curve, calculation representative point is exerted oneself;
C3, exert oneself and all quadrants sampling factor, calculate output of wind electric field, and then calculate exerting oneself of wind farm group according to representative point.
4. real-time predicting method for output of wind electric field according to claim 3 is characterized in that: press the wind direction difference, being divided into is 16 quadrants.
5. real-time predicting method for output of wind electric field according to claim 3 is characterized in that: described step c also comprises,
C4, utilize actual operating data checking wind farm group to exert oneself, if do not satisfy required precision, then execution in step c5;
C5, adjust the computation model of exerting oneself, recomputate.
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| CN104021424A (en) * | 2013-02-28 | 2014-09-03 | 国际商业机器公司 | Method and device used for predicting output power of blower in wind field |
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| CN103235981A (en) * | 2013-04-10 | 2013-08-07 | 东南大学 | Wind power quality trend predicting method |
| CN103235981B (en) * | 2013-04-10 | 2016-03-09 | 东南大学 | A kind of wind power quality trend forecasting method |
| CN104201712A (en) * | 2014-08-10 | 2014-12-10 | 东北电力大学 | Wind power real-time prediction calculation method base on spatial average wind speed |
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