CN102945508A - Model correction based wind power forecasting system and method - Google Patents
Model correction based wind power forecasting system and method Download PDFInfo
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Abstract
The invention relates to a model correction based wind power forecasting system and a method. The model correction based wind power forecasting system comprises a numerical weather forecasting server, a fan, an anemometer tower, a wind power forecasting server, a database server and a wind power forecasting system client. The model correction based wind power forecasting method comprises the following steps: firstly using a numerical weather forecasting model provided by the meteorological department to forecast the weather situation of a wind power plant, and then building a real-time forecasting model, and converting the forecasting value of the numerical weather forecasting model to power output of the wind power plant. The system and the method are adopted to reduce various model errors and ensure the accuracy rate of forecasting results.
Description
Technical field
The present invention relates to a kind of wind power prediction system and method, relate in particular to a kind of wind power prediction system and method based on model tuning.
Background technology
In the period that the newly-increased wind energy turbine set installed capacity of China is risen rapidly, the development of Wind Power Generation Industry has caused difficulty to power grid security and scheduling.In order to satisfy the electrical network needs, improve the wind-powered electricity generation utilization ratio, national correlation department has been put into effect series of standards and requirement, and the wind power forecast system is imperative installing and using of each large-scale wind field.
At present, China's Wind Power Generation Industry development time is shorter, and the wind power prediction method that industry is main and forecast system all are in to be groped and elementary developing stage.According to the regulation of national correlation department, the examination of existing wind power forecast system mainly from accuracy rate, report rate and 3 indexs of qualification rate to weigh.Accuracy rate wherein and qualification rate depend on the error of forecast power data and real power data.The flow process of industry main flow wind power forecast relates to the numerical value weather simulation, wind field anemometer tower data prediction, several key links such as wind energy turbine set capability forecasting.The Output rusults of above-mentioned each link can be as the input parameter of its next link, and the model error of any one link will be brought in the next link and go.This wherein several main model errors numerical value weather simulation error is arranged, anemometer tower, blower fan wind speed error in dipping, anemometer tower data and numerical weather forecast data correlation analytical error, it is representative that anemometer tower is surveyed wind data, power of fan curve deviation, wind field production capacity reduction evaluated error etc.
The wind power prediction is comparatively complicated physical simulation process.The model parameter of the main flow wind power forecasting system of industry generally is nonadjustable, and accuracy rate is difficult to guarantee with the project change so it predicts the outcome.
Several subject matters that exist of industry wind power forecast system have at present:
1, directly adopt representative anemometer tower predicted data to predict whole wind energy turbine set power: the hypotheses that adopts this method is that the measurement data such as the wind speed, wind direction, temperature, air pressure of anemometer tower will have suitable representativeness, can represent the data such as wind speed, wind direction of the point of each blower fan of wind field; In the complex-terrain wind energy turbine set, the anemometer tower wind regime can not replace the wind regime of all blower fan points, if still directly adopt in this case anemometer tower predicted data prediction wind energy turbine set power, its result will differ far away with actual conditions.
2, adopt the assurance wind-powered electricity generation unit powertrace prediction wind energy turbine set power of producer: in actual wind field, real power curve and the theoretical power curve of blower fan there are differences, this mainly is because there are larger difference in the fan condition of actual wind field and the operating mode under the theoretical test condition, in addition, the nacelle wind speed powertrace (NACP) of blower fan and real power curve be difference to some extent also, the powertrace that in the industry cycle directly adopts producer to provide in the part wind power forecast system is carried out prediction, and its result precision is difficult to guarantee.
3, the every reduction coefficient of wind energy turbine set is estimated to rely on engineering experience: in a specific wind field, outwardness reduction coefficient, mainly comprises: unit availability, powertrace, envirment factor etc.
Summary of the invention
Technical matters to be solved by this invention provides a kind of wind power prediction system and method based on model tuning, can effectively reduce the error of above-mentioned data in various model application processes of anemometer tower prediction wind regime data, wind power curve, the every reduction coefficient initiation of wind energy turbine set.
The technical scheme that the present invention solves the problems of the technologies described above is as follows: a kind of wind power prediction system based on model tuning, comprise numerical weather forecast server, blower fan, anemometer tower, wind power forecast server, database server, wind power forecast system client, described wind power forecast server is by the Internet(the Internet) or special line link to each other with numerical weather forecast server, blower fan, anemometer tower respectively, described data server links to each other with wind power forecast server, and described wind power forecast system client links to each other with described database server; Wind power forecast server obtains the numerical weather forecast data of numerical weather forecast service system, the weather data of anemometer tower, the real time execution information of blower fan and the communication with dispatch instructions of rationing the power supply that electrical network is assigned by Internet or private-line mode wind power is forecast, wind power forecast server stores into forecast result on the database server and in real time forecast result adopted national grid E form to upload to power grid security district II/III district.
A kind of wind power prediction methods based on model tuning, the numerical value Meteorological Forecast Model that at first utilizes meteorological department to provide, weather condition (mainly comprising the parameters such as wind speed, wind direction, temperature, air pressure, humidity) to wind energy turbine set is predicted, set up real-time prediction model, the predicted value of numerical value Meteorological Forecast Model is changed into the power stage of wind energy turbine set.Particularly, with WRF(weather forecast pattern) the simulation wind speed MOS(MOS method of forecast) be corrected to the prediction wind regime of anemometer tower point, the prediction wind regime of anemometer tower point is predicted by anemometer tower, then proofread and correct prediction wind regime to blower fan point by the prediction wind regime of anemometer tower point, the prediction wind regime of blower fan point and calibrated wind power forecast model are obtained wind energy turbine set prediction theory power, and the result after proofreading and correct in conjunction with the wind energy turbine set reduction coefficient again draws the real power of wind energy turbine set prediction.
The prediction wind regime of described blower fan point is proofreaied and correct and is utilized anemometer tower point prediction wind regime first to measure related last prediction with the blower fan historical data by MCP(in conjunction with anemometer tower again) method foundation association, anemometer tower place prediction wind regime is corrected to blower fan site estimation wind regime, the MCP method is: the supposition anemometer tower same period and the relation of blower fan data can be by mathematical simulations, for example linear and non-linear regression, variance ratio method.
The wind power forecast model of described correction is actual wind powertrace, the main flow process that described wind power forecast model is proofreaied and correct is: at first carry out the test of fan engine room powertrace and power coefficient calculating by blower fan theoretical power curve, then draw actual power of fan in conjunction with power coefficient in the prediction wind regime substitution fan engine room powertrace with blower fan point; Described power coefficient adopts back the production capacity of the historical fan engine room effective wind speed data substitution theoretical power curve of calculation and the difference of actual production capacity to calculate, wherein, the principle that data are rejected in the effective wind speed database mainly contains: 1, the external conditions such as wind speed exceed the working range of blower fan, 2, external condition exceeds the working range of testing apparatus, 3, blower fan be could not get on to the Net, 4 blower fan output powers are received the restriction such as external conditions such as electrical networks, 5, testing apparatus lost efficacy or performance reduces, 6, the 10min mean wind speed has exceeded beyond the sector of measuring, and 7, wind speed has exceeded the zone of reasonableness of nacelle wind speed transfer function.
Described wind energy turbine set reduction coefficient is proofreaied and correct and is comprised that availability is proofreaied and correct, electrical loss is proofreaied and correct, the environmental impact loss is proofreaied and correct;
Described availability is proofreaied and correct and is comprised that the time availability is proofreaied and correct and the production capacity availability is proofreaied and correct, it is by analyzing in the blower fan data system logout information to history that described time availability is proofreaied and correct, proofread and correct the time availability of each blower fan in conjunction with the shared time of different operating modes, it is defined as: the wind-powered electricity generation unit is at the nominal situation number percent of lower working time in one period; Described production capacity availability is proofreaied and correct the potential production capacity that comprises assessment wind-powered electricity generation unit, the actual production capacity of calculating the wind-powered electricity generation unit, utilize the fan engine room wind speed through after being converted to the unconfined flow leeward wind speed, the potential production capacity of the real power curve calculation wind-powered electricity generation unit in the test of substitution powertrace, production capacity can utilize Formulas to be expressed as:
Wherein, P
AvailBe production capacity availability, P
PotentialBe potential production capacity, P
ActualBe actual production capacity, in conjunction with the fan operation status information that the blower fan data monitoring system provides, based on the theory of information classification, the blower fan potential production capacity under the different conditions and actual production capacity carried out assessment, and then calculate the Seasonal fluctuation of availability;
Described electrical loss is proofreaied and correct and is adopted the electric weight that obtains wind energy turbine set endpiece electric energy metering machine, calculates the gross generation of wind energy turbine set wind-powered electricity generation unit, and the ratio of these two amounts is the reduction of electrical loss;
Described environmental impact be lost in the production capacity availability can't the situation of accurate Calculation under as the supplementary item of the reduction factor, mainly due to external environment condition (temperature, wind speed Hysteresis) etc. the production capacity of losing when parameter exceeds the fan design index, environmental impact loss is proofreaied and correct and is supposed that at first blower fan calculates production capacity under the condition that does not exceed environmental restraint, again calculates production capacity after introducing this envirment factor again; The rule that described environmental impact loss is proofreaied and correct is:
1, when wind speed during greater than cut-out wind speed, then replacing all production capacity data is 0;
2, when observed temperature when cutting out high temperature, then replacing all production capacity data is 0, when data before are 0 and observed temperature data during greater than incision high temperature, then replacing all production capacity data is 0;
3, be lower than when cutting out low temperature when observed temperature, then replacing all production capacity data is 0, when data before be 0 and the observed temperature data when again cutting low temperature, then replacing all production capacity data is 0.
The invention has the beneficial effects as follows: adopt a kind of wind power prediction system based on model tuning and method effectively to reduce error in the various model application processes that anemometer tower prediction wind regime data, wind power curve, the every reduction coefficient of wind energy turbine set cause, guaranteed the accuracy rate of forecast result.
Description of drawings
Fig. 1 is the Physical architecture figure that the present invention is based on the wind power prediction system of model tuning;
Fig. 2 is the wind power prediction methods process flow diagram that the present invention is based on model tuning;
Fig. 3 is the blower fan point prediction wind regime method flow diagram of model tuning anemometer tower prediction wind regime of the present invention;
Fig. 4 is the process flow diagram of wind-powered electricity generation unit theoretical power curve prediction of the present invention model tuning;
Fig. 5 is the wind power prediction process flow diagram that the present invention is based on model tuning;
Fig. 6 is the autoregressive coefficient of the statistical extrapolation training sample of ultra-short term forecast of the present invention.
Embodiment
Below in conjunction with accompanying drawing principle of the present invention and feature are described, institute gives an actual example and only is used for explaining the present invention, is not be used to limiting scope of the present invention.
As shown in Figure 1, Fig. 1 is a kind of topological structure of the wind power prediction system based on model tuning, comprise the numerical weather forecast server, blower fan, anemometer tower, wind power server, database server, wind power forecast system client, described wind power forecast server by Internet or special line respectively with the numerical weather forecast server, blower fan, anemometer tower links to each other, described data server links to each other with wind power forecast server, described wind power forecast system client links to each other with described database server, wind power forecast server obtains the numerical weather forecast data of numerical weather forecast service system by Internet or private-line mode, the weather data of anemometer tower, the communication with dispatch instructions of rationing the power supply that the real time execution information of blower fan and electrical network are assigned forecasts wind power, and wind power forecast server stores into forecast result on the database server and in real time forecast result adopted national grid E form to upload to power grid security district II/III district.
Fig. 2 is the wind power prediction methods process flow diagram based on model tuning, at first the simulation wind speed MOS of WRF forecast is corrected to the prediction wind regime of anemometer tower point, then proofread and correct prediction wind regime to blower fan point by the prediction wind regime of anemometer tower point, the wind power forecast model that the prediction wind regime substitution of blower fan point is calibrated obtains wind energy turbine set prediction theory power, result after proofreading and correct in conjunction with the wind energy turbine set reduction coefficient again calculates the real power that wind energy turbine set is predicted.Wherein, the flow process that is evaluated as the design of this project innovation point of the prediction wind regime of blower fan point, actual power of fan curve, the every reduction coefficient of wind energy turbine set.
The prediction wind regime method of blower fan point as shown in Figure 3, this figure is the flow process by the wind speed and direction association of MCP method foundation, according to landform, roughness of ground surface, the wind regime of wind field design phase, utilize anemometer tower point prediction wind regime to set up by MCP in conjunction with anemometer tower and blower fan historical data related, anemometer tower place prediction wind regime is corrected to blower fan point prediction wind regime.
The brief introduction of MCP method: the relation of the supposition anemometer tower same period and blower fan data can be by mathematical simulation, such as linear and non-linear regression, variance ratio method etc.
1. linearity and non-linear regression: take the same period anemometer tower data as independent variable, the same period, the data of blower fan were dependent variable, set up the regression relation of the two, linear regression is simple and ease for operation with it, and suitable precision and generally being adopted.
2, Variance ratio method just carried out the processing that a variance equates, the method has directly provided the expression formula of slope and intercept, and is convenient and simple, reasonable.
Wherein, ux, uy are anemometer tower and the blower fan point data set of the same period
Fig. 4 is the main process flow diagram of wind-powered electricity generation unit theoretical power curve prediction model tuning, at first carry out the test of fan engine room powertrace and power coefficient calculating by blower fan theoretical power curve, then with drawing actual power of fan performance in conjunction with power coefficient in the prediction wind regime substitution fan engine room powertrace of blower fan point, draw at last the wind energy turbine set predicted power.The powertrace testing process can be with reference to the IEC series standard, and power coefficient calculating adopts back the production capacity of the historical cabin effective wind speed data substitution theoretical power curve of calculation and the difference of actual production capacity to calculate.
Wherein, the principle that data are rejected in the effective wind speed database mainly contains:
(1) external condition such as wind speed exceeds the working range of blower fan,
(2) external condition exceeds the working range of testing apparatus,
(3) blower fan be could not get on to the Net,
(4) the blower fan output power is received the restriction such as external conditions such as electrical networks,
(5) testing apparatus lost efficacy or the performance reduction,
(6) the 10min mean wind speed has exceeded beyond the sector of measuring,
(7) wind speed has exceeded the zone of reasonableness of nacelle wind speed transfer function.
The correction of the every coefficient of wind energy turbine set: mainly comprised availability, electrical loss, environmental impact loss.
The availability estimation has comprised time availability and two evaluation methods of production capacity availability, wherein:
The time availability is calculated by analyzing in the blower fan data system logout information to history, the time availability that can proofread and correct each blower fan in conjunction with the shared time of different operating modes.It is defined as: the wind-powered electricity generation unit is at the nominal situation number percent of lower working time in one period.The correction of production capacity availability mainly comprises: the potential production capacity of assessment blower motor group, the actual production capacity of calculating wind-powered electricity generation unit.Usually the method that adopts is to utilize the fan engine room wind speed through after being converted to the unconfined flow leeward wind speed, the potential production capacity of the real power curve calculation wind-powered electricity generation unit in the test of substitution powertrace, and production capacity can utilize Formulas to be expressed as:
Wherein, P
PotentialBe potential production capacity; P
ActualBe actual production capacity.
In conjunction with the fan operation status information that the blower fan data monitoring system provides, based on the theory of information classification, the blower fan potential production capacity under the different conditions and actual production capacity are carried out assessment, and then calculate the Seasonal fluctuation of availability.
Electrical loss is proofreaied and correct and is adopted the electric weight that obtains wind energy turbine set endpiece electric energy metering machine, calculates the gross generation of wind energy turbine set wind-powered electricity generation unit, and the ratio of these two amounts is the reduction of electrical loss;
Environmental impact be lost in the production capacity availability can't the situation of accurate Calculation under as the supplementary item of the reduction factor, mainly due to external environment condition (temperature, wind speed) etc. the production capacity of losing when parameter exceeds the fan design index, environmental impact loss is proofreaied and correct and is supposed that at first blower fan calculates production capacity under the condition that does not exceed environmental restraint, again calculates production capacity after introducing this envirment factor again; The rule that described environmental impact loss is proofreaied and correct is:
(1), when wind speed during greater than cut-out wind speed, then replacing all production capacity data is 0;
(2), when observed temperature when cutting out high temperature, then replacing all production capacity data is 0, when data before are 0 and observed temperature data during greater than incision high temperature, then replacing all production capacity data is 0;
(3), when observed temperature is lower than when cutting out low temperature, then replacing all production capacity data is 0, when data before be 0 and the observed temperature data when again cutting low temperature, then replacing all production capacity data is 0.
Wind power prediction methods based on model tuning, this project adopts WRF (Weather Research Forecast) pattern to simulate, this modular system has many characteristics such as portable, easy care, extendible, effective and convenient, and more advanced numerical evaluation and Data Assimilation technology, moving multi nested grid performance and more perfect physical process (especially convection current and mesoscale Precipitation Process) will be arranged.It will help to carry out for numerical Simulation of High Resolution dissimilar, the different geographical synoptic process, improve resolution and the accuracy of weather forecast, so that new scientific payoffs applies to limited area operational forecasting model is more convenient.
What this research was selected is the WRFV3 version, adopts one deck grid, and mode top is positioned at: 31.0 ° of N, 112.5 ° of E.It is 201 * 182 that HORIZONTAL PLAID is counted, and horizontal resolution is 15km; Vertical direction has 35 layers; Time step is 60s.(can for different survey region adjustment) main physical process: WRF Single-Moment 6-class Microphysical scheme, the thermal diffusion scheme of Grell-Devenyi ensemble cumulus parameterization scheme, RRTM long-wave radiation scheme, Dudhia shortwave radiation scheme, ground layer scheme, four layers of soil, MRF boundary layer scheme etc.
In order to integrate with practical business, adopt NCEP forecast fields GFS data as WRF pattern initial fields and boundary condition, pattern is from 08 o'clock every day (during Beijing, lower same) begun to report, integration 84 hours is by a hour output analog physical amount (as: wind speed, temperature, air pressure, specific humidity, meridional wind, zonal wind, cloud amount etc.).Other physical quantity of pattern output can be used for the statistical correction of solar radiation model predictions output.
Fig. 5 is the wind power prediction system process flow diagram based on model tuning, among the figure, the in real time plan of rationing the power supply of blower fan data, numerical value weather data, real-time anemometer tower data and national grid all deposits database in, carry out short-time forecast and ultra-short term forecast through the wind-powered electricity generation forecast process system, then will forecast that structure uploads to national grid, then short-time forecast and ultra-short term forecast result be fed back to database.
1, short-term wind-electricity power prediction
Short-term wind-electricity power is predicted as the wind power output power prediction in following 3 days, and temporal resolution is 15min.According to the difference of Forecasting Methodology and condition of compatibility, substantially contain the various situations that the forecast of wind energy turbine set wind power may run into.
1.1, former logos
Former logos is first the wind field wind speed to be predicted, again through and historical data bring actual wind power curve into, finally obtain the power prediction value.This method can not need a large amount of, long-term measured data, more is applicable to complex-terrain.
Whether proofreading and correct the difference that reaches wind power forecasting model method for building up according to logarithm value simulation wind speed divides following methods to predict.
The actual power of fan curve of substitution match after WRF forecast wind speed MOS proofreaies and correct
Adopt wind field historical actual measurement air speed data and wind power data to set up the wind power forecast model, the simulation wind speed of WRF forecast is corrected to anemometer tower after, proofreaied and correct to every typhoon group of motors by the anemometer tower wind speed again.Bring prediction of wind speed into the wind power forecast model, obtain prediction theory power, in conjunction with the result after every reduction coefficient is proofreaied and correct, calculate the real power of wind energy turbine set prediction.This method is applicable to wind energy turbine set the anemometer tower data, and the situation of historical wind power data is also arranged, and Fig. 2 is the main process flow diagram of this method.
The model tuning of fixed effect
The wind power model tuning of adopting the historical numerical simulation data of a year and a day and corresponding wind power data to divide moon or setting up fixed effect season, and these correction parameters are preserved.
Wherein anemometer tower point and arbitrarily the MCP of blower fan point to be associated in certain period be mapping relations of comparatively fixing.The power characteristic of blower fan and the correction coefficient of every reduction also are and comparatively closely relation are arranged season.
(1) sets up data and the method for simulating the wind speed calibration model
(2) set up data and the method for wind power forecast model
Modeling data: wind energy turbine set actual measurement Wind Data, wind power data and the fan operation situation data of moving a year and a day.Actual unit wind power data need be with whole wind field 15min wind power divided by the start number of units.
Wind-powered electricity generation unit real power curve modeling method: adopt bin method that IEC recommends and based on the power characteristic measuring and calculation powertrace of nacelle wind speed meter.
1.2 power statistic law
The power statistic law is exactly to set up a kind of mapping relations between the power of input (measurement data of numerical value Meteorological Forecast Model, wind energy turbine set etc.) in system and wind energy turbine set, comprises linear and non-linear method, and autoregression technology, neural network etc. are specifically arranged.
The advantage of this method is to predict spontaneously to adapt to the wind energy turbine set position, so systematic error has reduced automatically.Shortcoming is to need long-term measurement data and extra training and ignored whole wind energy to the physical essence of electric energy conversion, under extreme weather conditions, system is difficult to Accurate Prediction, the correction of these rare weather conditions is predicted it is very important, otherwise will cause very large predicated error.
The rolling modeling
Adopt forecast a few days ago certain period numerical simulation data/anemometer tower survey wind data and roll every day with corresponding wind power data and set up the wind power forecast model, bring data into the wind power forecast model, just can obtain predicted power.This method is applicable to the situation that wind energy turbine set has nearly two months wind power data.
The modeling data: forecast ought push away 30 days numerical simulation data a few days ago, comprises wind speed (70m), wind direction sinusoidal (70m), wind direction cosine (70m), temperature (2m), surface pressure, humidity (2m); And corresponding wind power and fan operation situation data.
Mathematical Modeling Methods: multiple linear regression, neural net method
1.3 the method for continuing
When numerical simulation weather forecast data lacks newspaper, in the situation that above method all can't normally be moved, for guaranteeing that the prediction of wind energy turbine set wind power reports rate, can adopt the method for continuing to carry out the wind power forecast, namely use the live wind power result of proxima luce (prox. luc) as forecast result on the same day, this kind situation can only reach on the basis that not late newspaper fails to report as far as possible, and the artificial reference weather forecast is carried out experiential modification to improve forecast accuracy.
The prediction of 2 ultra-short terms
2.1 the real time correction based on short-term forecasting power
Based on the short-term wind-electricity power forecast result, utilize the live wind power data of real-time update, the short-term wind-electricity power forecast result is carried out real time correction.
Set up model:
In the formula, i is that ultra-short term gives the correct time time in advance, Y
iTime ultra-short term power prediction during for i, F
iTime short term power prediction during for i,
Be live wind power mean value of the past period,
Be the past period short term power predicted mean vote.
Model adopts time (15 minutes) rolling foundation when pursuing,
All get over arithmetic mean value in 2 hours.
When there are certain systematic bias in short-time forecast result and live power, the method effect is more satisfactory.
2.2 statistical extrapolation
Utilize recently 200 left and right sides samples constantly, set up respectively 16 auto-regressive equations, predictor is respectively and lags behind 15,30,45 ... 240 minutes the after-power factor.
Auto-regressive equation: Y
t=a
iY
T-i+ b
i, in the formula, Y is live power, and is inferior when i is hysteresis, by training sample Coefficient of determination a, b, as shown in Figure 6.For improving forecast accuracy, equation coefficient should obtain by each training of rolling.
The above only is preferred embodiment of the present invention, and is in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, is equal to replacement, improvement etc., all should be included within protection scope of the present invention.
Claims (6)
1. wind power prediction system based on model tuning, it is characterized in that: comprise numerical weather forecast server, blower fan, anemometer tower, wind power forecast server, database server, wind power forecast system client, described wind power forecast server links to each other with numerical weather forecast server, blower fan, anemometer tower respectively by Internet or special line, described data server links to each other with wind power forecast server, and described wind power forecast system client links to each other with described database server.
2. wind power prediction methods based on model tuning is characterized in that:
At first the simulation wind speed of numerical weather prediction model forecast is corrected to the prediction wind regime of anemometer tower point;
Then proofread and correct prediction wind regime to blower fan point by the prediction wind regime of anemometer tower point, the prediction wind regime of blower fan point and calibrated wind power forecast model obtain wind energy turbine set prediction theory power, and the result after proofreading and correct in conjunction with the wind energy turbine set reduction coefficient again draws the real power of wind energy turbine set prediction.
3. a kind of wind power prediction methods based on model tuning according to claim 2, it is characterized in that: described anemometer tower prediction wind regime Data correction utilizes anemometer tower point prediction wind regime, anemometer tower historical data and blower fan historical data to set up association between the wind speed and direction by the method for measuring first more related last prediction, and anemometer tower place prediction wind regime is corrected to blower fan point prediction wind regime.
4. according to claim 2 or 3 described a kind of wind-powered electricity generation prediction methods based on model tuning, it is characterized in that: the main flow process that described wind power forecast model is proofreaied and correct is: at first carry out the test of fan engine room powertrace and power coefficient calculating by blower fan theoretical power curve, then draw actual power of fan in conjunction with power coefficient in the prediction wind regime substitution fan engine room powertrace with blower fan point.
5. a kind of wind power prediction methods based on model tuning according to claim 4 is characterized in that: described power coefficient adopts back the production capacity of calculating historical fan engine room effective wind speed data substitution theoretical power curve and the difference of actual production capacity to calculate.
6. it is characterized in that according to claim 2 or 3 described a kind of wind power prediction methods based on model tuning: described wind energy turbine set reduction coefficient is proofreaied and correct and is comprised that availability is proofreaied and correct, electrical loss is proofreaied and correct, the environmental impact loss is proofreaied and correct; Described availability is proofreaied and correct and is comprised that the time availability is proofreaied and correct and the production capacity availability is proofreaied and correct, it is by analyzing in the blower fan data system logout information to history, the time availability of proofreading and correct each blower fan in conjunction with the shared time of different operating modes that described time availability is proofreaied and correct; Described production capacity availability is proofreaied and correct the potential production capacity that comprises assessment wind-powered electricity generation unit, the actual poor energy that calculates the wind-powered electricity generation unit, utilize the fan engine room wind speed through after being converted to the unconfined flow leeward wind speed, the potential production capacity of the real power curve calculation wind-powered electricity generation unit in the test of substitution powertrace, production capacity availability Formulas is expressed as:
Wherein, P
AvailBe production capacity availability, P
PotentialBe potential production capacity, P
ActualBe actual production capacity, in conjunction with the fan operation status information that the fan monitoring system provides, based on the theory of information classification, the blower fan potential production capacity under the different conditions and actual production capacity carried out assessment, and then calculate the Seasonal fluctuation of availability;
Described electrical loss is proofreaied and correct and is adopted the electric weight that obtains wind energy turbine set endpiece electric energy metering machine, calculates the gross generation of wind energy turbine set wind-powered electricity generation unit, and the ratio of these two amounts is the reduction of electrical loss;
The loss of described environmental impact is proofreaied and correct and is supposed that at first blower fan calculates production capacity under the condition that does not exceed environmental restraint, again calculates production capacity after introducing this envirment factor again.
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