CN103489046A - Method for predicting wind power plant short-term power - Google Patents
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Abstract
The invention provides a method for predicting wind power plant short-term power. The method includes the steps of firstly, setting up a wind speed statistical downscaling model of a single draught fan; secondly, generating a predicted wind speed at the height of a hub in the position where the single draught fan is located according to predicting factors of mesoscale numerical weather prediction for a wind power plant area in 48 hours and the statistical downscaling model of the single draught fan in the first step; thirdly, setting up a wind speed, wind direction-power model of each draught fan, and obtaining power prediction of each draught fan according to a wind direction predicted through the mesoscale numerical weather prediction and a wind speed predicted in the second step. According to the method, the uncertainty caused by lack of mesoscale resolution is effectively reduced, and the prediction accuracy of the wind power plant short-term power is remarkably improved.
Description
Technical field
The present invention relates to a kind of method for forecasting short-term power in wind power station.
Background technology
In recent years, along with China energy policy is adjusted, grid connected wind power installed capacity rapid growth, large-scale wind power is concentrated and grid-connected electric power netting safe running is brought to impact.Improve the predictability of output of wind electric field, can effectively reduce the impact that wind-powered electricity generation causes electrical network, alleviate dispatching of power netwoks peak regulation pressure, this is for taking full advantage of wind energy resources, and further improving grid connected wind power installation ratio has positive effect.According to the domestic wind farm power prediction technical manual of having issued and implemented, wind energy turbine set must every day the time is uploaded exert oneself prediction curve accept the precision of prediction examination of following 24h to power dispatching station according to the rules.In order accurately to reflect air motion state next day, must use the input data of mesoscale numerical weather forecast (numerical weather forecast) pattern output as wind energy turbine set short term power prognoses system, so mesoscale numerical weather prediction model prediction output accuracy has determined the precision of wind energy turbine set short term power prediction to a great extent.Yet, mesoscale model atmospheric physics procedure parameter scheme can not effectively be simulated time grid yardstick (being less than 1km) atmospheric physics process, there are error in synoptic process and the truth of its description, and this error can increase along with the growth of pattern integral operation time.Therefore, grid resolution is not enough describes inaccurate meeting with the atmospheric physics procedure parameter scheme relevant with resolution and mesoscale model is predicted the outcome exist uncertain.Directly apply to power prediction and can bring larger uncertainty, must be fallen the yardstick pre-service to it.
Two kinds of methods of the main employing of yardstick research are fallen in centering yardstick numerical weather forecast output at present: 1, use physical model to solve the impact of wind energy turbine set Local factor on air-flow.This method calculation cost is less, but physical model structure and implementation procedure are comparatively complicated, and precision improves limited.
2, adopt power to fall two time scales approach, for example Fluid Mechanics Computation (CFD) is simulated wind energy turbine set interior flow field evolution process, this method can obtain comparatively accurately wind speed profile, but need to use the CFD method to solve the Navier-Stokes equation when setting up prediction of wind speed Query Database or direct prediction of wind speed, Project Realization complexity and calculation cost are huge, high to hardware requirement.
Summary of the invention
In view of this, fundamental purpose of the present invention is, a kind of method for forecasting short-term power in wind power station is provided, this paper falls two time scales approach by statistics and is incorporated in wind energy turbine set short term power forecasting techniques, solved preferably the larger problem of numerical weather forecast air speed error, effectively reduce the uncertainty that the mesoscale lack of resolution is brought, greatly improved the mesoscale numerical weather forecast and fallen dimension calculation efficiency, significantly improved wind energy turbine set short term power precision of prediction.
Comprise step:
Scale Model falls in A, the wind speed statistics of setting up the separate unit blower fan;
B, according to the predictor of the following 48 hours mesoscale numerical weather forecasts in wind energy turbine set zone, and in steps A, Scale Model falls in separate unit blower fan statistics, generates the prediction of wind speed of separate unit blower fan position hub height;
C, set up the wind speed of every Fans, the model of wind direction-power, the wind speed that the wind direction of predicting according to the mesoscale numerical weather forecast and step B predict, draw the power prediction of every Fans.
By upper, set up statistics by historical data and fall Scale Model, mesoscale pattern count value weather forecast wind speed is added up and fallen yardstick, draw the every Fans of wind energy turbine set position hub height prediction of wind speed, model in conjunction with wind speed, wind direction-power is exerted oneself and is predicted every Fans, realizes the short-term forecasting of exerting oneself of wind energy turbine set integral body.Above-mentioned Forecasting Methodology has effectively reduced the uncertainty that the mesoscale lack of resolution is brought, and has significantly improved wind energy turbine set short term power precision of prediction.
Optionally, in described steps A, utilize the BP neural network, by the predictor of mesoscale numerical weather forecast and the historical data between separate unit axial fan hub height actual measurement wind speed, described BP neural network is trained.
By upper, adopt the short-term wind speed forecasting model of BP neural network separate unit blower fan position hub height, can greatly reduce the complexity of setting up model, thereby reduce operand, and to the dependence of high-performance hardware,, and then significantly save estimated cost.
Optionally, the predictor that the input layer of described BP neural network is the mesoscale numerical weather forecast, output layer is separate unit axial fan hub height actual measurement wind speed.。
The predictor of described mesoscale numerical weather forecast at least comprises: the predictor of described mesoscale numerical weather forecast at least comprises: wind speed, wind direction, pressure and the relative humidity of 500hPa geopotential unit, 850hPa geopotential unit, axial fan hub height.
By upper, because above-mentioned predictor can be predicted comparatively accurately, and between different predictor, be weak relevant or irrelevant, therefore the input quantity using above-mentioned predictor as the BP neural network, can be trained the BP neural network comparatively accurately.
Optionally, described step C comprises:
C1, set up wind direction, wind speed that each blower fan is surveyed and, the historical data base of the active power value of blower fan under above-mentioned wind direction and wind speed;
C2, for different wind directions, between the wind speed setting acquisition zone, and the lower active power data that gather between each wind speed acquisition zone are carried out to pre-service;
C3, set up the corresponding model of wind direction, wind speed and power;
The wind speed that C4, the wind direction of predicting according to the mesoscale numerical weather forecast and step B predict, the wind direction in integrating step C3, wind speed-power module draw the power prediction of every Fans.
By upper, set up the power statistic table model of separate unit blower fan by historical data, and the wind direction forecast by Study of Meso Scale Weather prediction and the described forecasting wind speed of step B above, the power of every Fans is predicted, realize the overall power prediction to the wind-powered electricity generation unit.Effectively reduce the uncertainty that the mesoscale lack of resolution is brought, significantly improved the wind farm power prediction precision of prediction.
Optionally, described step C2 comprises:
C21, the active power value in each wind speed interval is sorted from small to large;
C22, the power upper limit of determining active power in each wind speed interval and power lower limit;
C23, active power value in each wind speed interval is rejected lower than the power lower limit with higher than the data of power upper limit and corresponding actual measurement air speed data.
By upper, by power upper limit being set and the power lower limit is rejected the abnormal data in historical data, thereby make wind speed-power characteristic more truly reflect the actual set performance.
Optionally, described wind direction be take 22.5 ° as an interval, is divided into altogether 16 intervals.
By upper, to wind speed be divided into 16 each wait by stages, and then the blower fan of diverse location is pressed to the wind direction interval division, to realize the wind direction distribution.
Optionally, described numerical weather forecast predictor, be no less than 6 calendar months with the wind speed of the actual measurement of separate unit axial fan hub height wind speed, active power data and the anemometer tower of numerical weather forecast predictor time match, the time span of wind direction data.
By upper, only by the historical data of half a year, just can set up the wind energy turbine set Short-term Forecasting Model, improving mesoscale numerical weather forecast and short term power precision of prediction simultaneously, greatly reduce the mesoscale numerical weather forecast and fallen the yardstick operand, reduced the system hardware cost.
The accompanying drawing explanation
The process flow diagram that Fig. 1 is method for forecasting short-term power in wind power station of the present invention;
The principle schematic that Fig. 2 is BP neural network of the present invention;
The former factory atmosphere speed that Fig. 3 is the separate unit blower fan-power characteristic schematic diagram.
Embodiment
Method for forecasting short-term power in wind power station provided by the present invention is based on the prediction output of adding up the method centering yardstick numerical weather forecast that falls yardstick and is fallen the yardstick computing, the matching relationship of the wind speed by setting up numerical weather forecast and separate unit blower fan position hub height, draw the short-term forecasting wind speed of separate unit blower fan position hub height, and then realize the short term power of every Fans is predicted by cargo tracer Fans power statistic table model, to realize the short-term forecasting that wind energy turbine set integral body is exerted oneself.Above-mentioned Forecasting Methodology has effectively reduced the uncertainty in traffic brought due to mesoscale number of degrees value weather forecast lack of resolution, has significantly improved the wind farm power prediction precision of prediction.
As shown in Figure 1, method for forecasting short-term power in wind power station comprises:
Step 10: collect each anemometer tower actual measurement wind speed, wind direction data, the blower fan of time match is surveyed wind speed, active power data with it, and the historical data of wind energy turbine set zone mesoscale numerical weather forecast predictor.
The selection of mesoscale numerical weather forecast predictor is that factor important in two time scales approach falls in statistics, and the selection of predictor has determined the Forecast characteristic of the local meteorological condition of wind energy turbine set to a great extent.Choose the predictor that wind speed is had to appreciable impact, reduce its quantity, can effectively reduce and set up the complexity that Scale Model falls in statistics, reduce the model calculated amount, avoid introducing extra interfere information.Choose predictor and follow following methods:
1, predictor is predicted more exactly by the mesoscale numerical weather prediction model, and has significant Nonlinear Statistical relation between predictor and wind energy turbine set domestic site meteorological element, and this statistical relationship is stable and effective;
2, predictor must be able to reflect important Mesoscale physical change process;
3, between different predictor, be weak relevant or irrelevant.
Based on above-mentioned some, selected predictor is respectively: wind speed, wind direction, pressure and the relative humidity of the 500hPa geopotential unit that the mesoscale numerical weather forecast provides, 850hPa geopotential unit, axial fan hub height.
Above-mentioned anemometer tower reaches the blower fan historical data time span of time match with it and is at least half a year, preferably, is 1 year.
Step 20: use the statistics of BP neural network separate unit blower fan position hub height to fall Scale Model.
Use the statistical relationship between BP neural network mesoscale numerical weather forecast predictor and separate unit blower fan position hub height wind speed, this statistical relationship is fallen to Scale Model as the statistics of separate unit blower fan, be applied to produce this blower fan position hub height prediction of wind speed.
The BP neural network is a kind of Multi-layered Feedforward Networks by the training of error backpropagation algorithm, can approach any Nonlinear Mapping with arbitrary accuracy, and its topological structure is comprised of input layer, hidden layer and output layer, and this model is output as:
in formula, the t that y (t) is model output is separate unit axial fan hub height forecasting wind speed value constantly; F is transport function, and in the present embodiment, transport function is the tangent hyperbolic function; w
jfor connecting the weight coefficient of hidden layer and output layer; x
i(t) be mode input t i predictor value constantly; v
ijfor connecting the weight coefficient of input layer and hidden layer; N is the input layer dimension; The dimension that m is hidden layer; θ
jfor the hidden layer threshold value; θ
0for the output layer threshold value.
Take and set up separate unit blower fan statistics and fall Scale Model and illustrate as example, the mesoscale numerical weather prediction model predictor output that modeling data is year June in January, 2012 to 2012 and blower fan actual measurement air speed data, data time resolution is 15min, data length is chosen 0～24h next day, using wind speed, wind direction, pressure and the relative humidity of the 500hPa geopotential unit of pattern output, 850hPa geopotential unit, axial fan hub height as the model training input quantity, axial fan hub height wind speed measured value, as the training output quantity, is set up the BP neural network model of three layers.This BP network input layer number is 6; The hidden layer neuron number is preferentially determined through test; Network output layer neuron number is 1.During training BP neural network, the input and output layer data is carried out to normalized, adopts following method to carry out normalized here:
the pressure of take describes as example, and X means to predict pressure values, X
minmean default minimum pressure value, X
maxmean default maximum pressure value,
mean normalized pressure values.Analyze through screening, determine that the hidden layer neuron number is at 21 o'clock, training sample error minimum, now weight coefficient matrix and threshold matrix are also determined thereupon.BP neural network model after weighted value and threshold matrix parameter identification falls the scale prediction model as the statistics of blower fan, and the predicted value of to this statistics, falling the Scale Model input prediction factor just can obtain the short-term forecasting air speed value of separate unit blower fan position hub height.
Step 30: fall according to separate unit blower fan statistics the wind energy turbine set zone short-term forecasting wind speed that Scale Model and mesoscale numerical weather forecast provide, generate separate unit blower fan position hub height short-term forecasting wind speed.
After completing separate unit blower fan statistics and falling the yardstick modeling, Scale Model, exportable separate unit blower fan position axial fan hub height short-term forecasting wind speed are fallen in the predicted value (500hPa geopotential unit, 850hPa geopotential unit, axial fan hub height wind speed, wind direction, pressure and relative humidity) of the mesoscale numerical weather forecast predictor of following 24 or 48 hours input statistics.
Two time scales approach falls in above-mentioned mesoscale numerical weather forecast statistics, use above-mentioned 6 predictor just can calculate the forecasting wind speed value of separate unit blower fan position hub height, calculated amount is little and computing velocity is fast, can meet the requirement of wind energy turbine set short term power predictive engine fully, the method is fallen the yardstick technology than existing power, greatly improves counting yield.
Step 40: the power statistic table model of setting up the separate unit blower fan.
Specifically comprise step:
Step 401: set up that the wind energy turbine set anemometer tower determines the wind direction, wind speed that each blower fan is surveyed and the historical data base of the active power value of blower fan under above-mentioned wind direction and wind speed.Wherein, the time span of historical data base is 1 year.
Step 402: corresponding different wind direction intervals, the wind speed setting interval, and corresponding active power data under each wind speed interval are carried out to pre-service, reject bad data points.
Prediction of wind speed is converted into to predicted power and must sets up blower fan wind speed – power mapping relations under actual condition, Fig. 3 reflects that former factory atmosphere speed-power characteristic that blower fan producer provides can not describe the input/output relation of blower fan under actual condition well, and the loose point correspondence of the wind speed-power of the blower fan that is incorporated into the power networks is distributed in a wider zone, in order to hold on the whole the relation of wind speed and power, must be processed wind speed-power scatter diagram, be set up the wind speed that precision is higher-power mapping relations.In the present embodiment, the startup wind speed of blower fan is 3/s, and cut-out wind speed is 25/s, take 0.1/s as the interval step-length of wind speed, and the wind speed interval is [3-3.1], [3.1-3.2], and [3.2-3.3] ..., [24.8-24.9], [24.9-25], totally 220 wind speed intervals.
[3-3.1] wind speed of take is interval is the example explanation, at first needs that misoperation data point in wind speed-power coordinate system is rejected to bad point and processes.Because the blower fan group is shut down maintenance; blower fan group operation exception; the reasons such as air velocity transducer is malfunctioning; make in wind speed and power coordinate system and contain a large amount of misoperation data point (being scattered in the data point of wind speed in the discrete Fig. 3 of being distributed in-power characteristic edge), these abnormity point can have a strong impact on the effect of setting up wind speed-power mapping relations usually.The process of rejecting bad point comprises: all active power values to [3-3.1] wind speed interval sort from small to large, calculate the mean value of active power, power typical value as this wind speed interval, choose come back a certain performance number as power upper limit, come a certain performance number of front as the power lower limit.The upper and lower bound of rejecting bad point in each wind speed interval is revised, the active power value of predicting in each wind speed interval is rejected lower than lower limit with higher than the data point of the upper limit, to complete the rejecting bad point.For example, choose and be positioned at 99%(in the wind speed interval and be 100% to the maximum, minimum is 1%) the active power value located is as power upper limit, is positioned at 1%(and is 100% to the maximum, minimum is 1%) the active power value located is as the power lower limit.All do aforesaid operations for each wind speed interval, determine power upper limit and power lower limit in each wind speed interval.
Step 403: set wind direction, wind speed interval.
In the present embodiment, wind direction be take to 22.5 ° as an interval, being divided into is 16 intervals, and wherein, 0 °～22.5 ° is D
1interval, 22.6 °～50 ° be D
2interval, by that analogy, 337.6 °～360 ° is D
16interval.In addition, also the wind direction interval can be divided into to 32 or 64, subregion is more, and precision is higher, and corresponding operand is larger, consuming time longer.Each blower fan is divided into groups according to the by stages such as above-mentioned.Be directed to the blower fan be positioned on mean line, an interval after being attributed to.
In addition, in this step, the startup wind speed of take is 3/s, and cut-out wind speed is that 25/s is example, and setting 0.5/s is the interval step-length of wind speed, and being divided into is 44 wind speed intervals.
Step 404: the power statistic table model of setting up the separate unit blower fan.
Set up the power module of blower fan 1, as shown in table 1 below, horizontal ordinate is D
1～D
1616 wind direction intervals altogether; Ordinate is B
1～B
4444 wind speed intervals altogether.
With the interval D of wind direction
1, the wind speed interval B
1for example, calculate under this condition, the mean value of separate unit blower fan active power value, as this power of fan eigenwert; In like manner calculate the interval D of wind direction
1, the wind speed interval B
2～B
44under separate unit power of fan eigenwert, by that analogy, complete the foundation of wind direction shown in table 1, wind speed-power module.
| ? | D 1 | D 2 | D 3 | D 4 | D 5 | … | D 15 | D 16 |
| B 1 | P 0101 | P 0102 | P 0103 | P 0104 | P 0105 | … | P 0115 | P 0116 |
| B 2 | P 0201 | P 0202 | P 0203 | P 0204 | P 0205 | … | P 0215 | P 0216 |
| B 3 | P 0301 | P 0302 | P 0303 | P 0304 | P 0305 | … | P 0315 | P 0316 |
| … | … | … | … | … | … | … | … | … |
| B 44 | P 4401 | P 4402 | P 4403 | P 4404 | P 4405 | … | P 4415 | P 4416 |
Table 1
Step 405: the wind direction of predicting according to numerical weather forecast, and in step 30 separate unit blower fan position hub height statistics to fall the yardstick wind speed be querying condition, obtain short-term forecasting power to separate unit power of fan statistical form model.
Step 50: the wind energy turbine set short term power is predicted.
The separate unit blower fan position hub height prediction of wind speed that two time scales approach obtains is fallen in statistics, and the wind direction of predicting according to numerical weather forecast inputs the inquiry of this separate unit power of fan statistical form, just can obtain the blower fan short-term forecasting power under any wind direction and wind speed.
Further, owing to producing electricity consumption in the running of wind generating set process, lose, therefore the predicted power-electricity consumption of wind-powered electricity generation unit output power=all blower fans is lost.
Preferably, also comprise that step 60(is not shown), wind-powered electricity generation unit control end, according to the output power of each separate unit blower fan of predicting in step 50, is regulated its online power.For instance, when the wind-powered electricity generation unit runs into limit while exerting oneself state, wind-powered electricity generation unit control end is controlled the higher several Fans of output power and is stopped power online output, then is that electric energy is stored by power transfer.
The foregoing is only preferred embodiment of the present invention, in order to limit the present invention, within the spirit and principles in the present invention not all, any modification of doing, be equal to replacement, improvement etc., within all should being included in protection scope of the present invention.
Claims (8)
1. a method for forecasting short-term power in wind power station, is characterized in that, comprises step:
Scale Model falls in A, the wind speed statistics of setting up the separate unit blower fan;
B, according to the predictor of the following 48 hours mesoscale numerical weather forecasts in wind energy turbine set zone, and in steps A, Scale Model falls in separate unit blower fan statistics, generates the prediction of wind speed of separate unit blower fan position hub height;
C, set up the wind speed of every Fans, the model of wind direction-power, the wind speed that the wind direction of predicting according to the mesoscale numerical weather forecast and step B predict, draw the power prediction of every Fans.
2. power forecasting method according to claim 1, it is characterized in that, in described steps A, utilize the BP neural network, by the predictor of mesoscale numerical weather forecast and the historical data between separate unit axial fan hub height actual measurement wind speed, described BP neural network is trained.
3. Forecasting Methodology according to claim 2, is characterized in that, the predictor that the input layer of described BP neural network is the mesoscale numerical weather forecast, and output layer is separate unit axial fan hub height actual measurement wind speed.
4. power forecasting method according to claim 3, it is characterized in that, the predictor of described mesoscale numerical weather forecast at least comprises: wind speed, wind direction, pressure and the relative humidity of 500hPa geopotential unit, 850hPa geopotential unit, axial fan hub height.
5. power forecasting method according to claim 1, is characterized in that, described step C comprises:
C1, set up wind direction, wind speed that each blower fan is surveyed and, the historical data base of the active power value of blower fan under above-mentioned wind direction and wind speed;
C2, for different wind directions, between the wind speed setting acquisition zone, and the lower active power data that gather between each wind speed acquisition zone are carried out to pre-service;
C3, set up the corresponding model of wind direction, wind speed and power;
The wind speed that C4, the wind direction of predicting according to the mesoscale numerical weather forecast and step B predict, the wind direction in integrating step C3, wind speed-power module draw the power prediction of every Fans.
6. power forecasting method according to claim 5, is characterized in that, described step C2 comprises:
C21, the active power value in each wind speed interval is sorted from small to large;
C22, the power upper limit of determining active power in each wind speed interval and power lower limit;
C23, active power value in each wind speed interval is rejected lower than the power lower limit with higher than the data of power upper limit and corresponding actual measurement air speed data.
7. power forecasting method according to claim 6, is characterized in that, described wind direction be take 22.5 ° as an interval, is divided into altogether 16 intervals.
8. power forecasting method according to claim 1, it is characterized in that, described numerical weather forecast predictor, with the wind speed of the separate unit axial fan hub height of numerical weather forecast predictor time match actual measurement wind speed, active power data and anemometer tower, the time span of wind direction data, be no less than 6 calendar months.
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104794347A (en) * | 2015-04-22 | 2015-07-22 | 中国大唐集团新能源股份有限公司 | Wind turbine generator set engine room transfer function subregional fitting method |
| CN105117788A (en) * | 2015-07-22 | 2015-12-02 | 河南行知专利服务有限公司 | Wind power generation power prediction method |
| CN105205564A (en) * | 2015-09-29 | 2015-12-30 | 沈阳工程学院 | Wind power plant wind curtailment electric quantity statistical system and method based on anemometer tower neural network |
| CN105205544A (en) * | 2014-06-24 | 2015-12-30 | 华北电力大学（保定） | Wind power prediction method based on bi-random theory |
| CN106650977A (en) * | 2015-10-29 | 2017-05-10 | 中能电力科技开发有限公司 | Short-term power prediction method used for newly-built wind farm |
| CN106875037A (en) * | 2016-12-30 | 2017-06-20 | 东软集团股份有限公司 | Wind-force Forecasting Methodology and device |
| CN107977730A (en) * | 2017-09-18 | 2018-05-01 | 沈阳大学 | A kind of wind measurement method of multisensor Data Fusion technology |
| CN108167133A (en) * | 2017-12-20 | 2018-06-15 | 北京金风慧能技术有限公司 | Training method, device and the wind power generating set of air speed error model |
| CN108364071A (en) * | 2018-02-06 | 2018-08-03 | 南京国电南自电网自动化有限公司 | A kind of adaptive modeling wind power prediction method based on genetic programming algorithm |
| CN110070132A (en) * | 2019-04-24 | 2019-07-30 | 华中科技大学无锡研究院 | The correction-compensation method of wind power plant SCADA system missing data reparation result |
| CN110442836A (en) * | 2019-07-23 | 2019-11-12 | 国能日新科技股份有限公司 | Short-term wind speed forecasting method of wind farm and system |
| CN110598911A (en) * | 2019-08-21 | 2019-12-20 | 同济大学 | Wind speed prediction method for wind turbine of wind power plant |
| CN113205210A (en) * | 2021-04-29 | 2021-08-03 | 西安热工研究院有限公司 | Wind speed and power prediction method, system, equipment and storage medium for wind power plant with complex terrain |
| US11105958B2 (en) * | 2018-12-28 | 2021-08-31 | Utopus Insights, Inc. | Systems and methods for distributed-solar power forecasting using parameter regularization |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101592538A (en) * | 2009-06-18 | 2009-12-02 | 东北电力大学 | A kind of computing method based on measured data wind energy turbine set steady-state output power |
| KR20100116182A (en) * | 2008-02-08 | 2010-10-29 | 츠다코마 고교 가부시키가이샤 | Spindle head for machine tool |
| CN102588210A (en) * | 2011-12-21 | 2012-07-18 | 中能电力科技开发有限公司 | Filtering method for preprocessing fitting data of power curve |
| CN103001249A (en) * | 2012-11-15 | 2013-03-27 | 中国电力科学研究院 | Method for predicting short-term power of wind farm on the basis of BP (back propagation) neural network |
-
2013
- 2013-09-29 CN CN201310456316.7A patent/CN103489046A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100116182A (en) * | 2008-02-08 | 2010-10-29 | 츠다코마 고교 가부시키가이샤 | Spindle head for machine tool |
| CN101592538A (en) * | 2009-06-18 | 2009-12-02 | 东北电力大学 | A kind of computing method based on measured data wind energy turbine set steady-state output power |
| CN102588210A (en) * | 2011-12-21 | 2012-07-18 | 中能电力科技开发有限公司 | Filtering method for preprocessing fitting data of power curve |
| CN103001249A (en) * | 2012-11-15 | 2013-03-27 | 中国电力科学研究院 | Method for predicting short-term power of wind farm on the basis of BP (back propagation) neural network |
Non-Patent Citations (2)
| Title |
|---|
| 蔡祯祺: "《基于数值天气预报NWP修正的BP神经网络风电功率短期预测研究》", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 * |
| 谷兴凯: "《风电功率预测技术综述》", 《电网技术》 * |
Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105205544A (en) * | 2014-06-24 | 2015-12-30 | 华北电力大学（保定） | Wind power prediction method based on bi-random theory |
| CN105205544B (en) * | 2014-06-24 | 2018-07-24 | 华北电力大学（保定） | A kind of wind power forecasting method based on dual random theory |
| CN104794347B (en) * | 2015-04-22 | 2017-08-29 | 中国大唐集团新能源股份有限公司 | A kind of wind turbine cabin transmission function Separate Fit method |
| CN104794347A (en) * | 2015-04-22 | 2015-07-22 | 中国大唐集团新能源股份有限公司 | Wind turbine generator set engine room transfer function subregional fitting method |
| CN105117788A (en) * | 2015-07-22 | 2015-12-02 | 河南行知专利服务有限公司 | Wind power generation power prediction method |
| CN105205564A (en) * | 2015-09-29 | 2015-12-30 | 沈阳工程学院 | Wind power plant wind curtailment electric quantity statistical system and method based on anemometer tower neural network |
| CN106650977A (en) * | 2015-10-29 | 2017-05-10 | 中能电力科技开发有限公司 | Short-term power prediction method used for newly-built wind farm |
| CN106875037B (en) * | 2016-12-30 | 2020-11-10 | 东软集团股份有限公司 | Wind power prediction method and device |
| CN106875037A (en) * | 2016-12-30 | 2017-06-20 | 东软集团股份有限公司 | Wind-force Forecasting Methodology and device |
| CN107977730A (en) * | 2017-09-18 | 2018-05-01 | 沈阳大学 | A kind of wind measurement method of multisensor Data Fusion technology |
| CN108167133A (en) * | 2017-12-20 | 2018-06-15 | 北京金风慧能技术有限公司 | Training method, device and the wind power generating set of air speed error model |
| CN108364071A (en) * | 2018-02-06 | 2018-08-03 | 南京国电南自电网自动化有限公司 | A kind of adaptive modeling wind power prediction method based on genetic programming algorithm |
| US11105958B2 (en) * | 2018-12-28 | 2021-08-31 | Utopus Insights, Inc. | Systems and methods for distributed-solar power forecasting using parameter regularization |
| US11689154B2 (en) | 2018-12-28 | 2023-06-27 | Utopus Insights, Inc. | Systems and methods for distributed-solar power forecasting using parameter regularization |
| CN110070132A (en) * | 2019-04-24 | 2019-07-30 | 华中科技大学无锡研究院 | The correction-compensation method of wind power plant SCADA system missing data reparation result |
| CN110070132B (en) * | 2019-04-24 | 2022-06-10 | 华中科技大学无锡研究院 | Correction and compensation method for wind power plant SCADA system missing data repair result |
| CN110442836A (en) * | 2019-07-23 | 2019-11-12 | 国能日新科技股份有限公司 | Short-term wind speed forecasting method of wind farm and system |
| CN110442836B (en) * | 2019-07-23 | 2023-05-12 | 国能日新科技股份有限公司 | Wind power plant short-term wind speed prediction method and system |
| CN110598911A (en) * | 2019-08-21 | 2019-12-20 | 同济大学 | Wind speed prediction method for wind turbine of wind power plant |
| CN110598911B (en) * | 2019-08-21 | 2022-06-21 | 同济大学 | Wind speed prediction method for wind turbine of wind power plant |
| CN113205210A (en) * | 2021-04-29 | 2021-08-03 | 西安热工研究院有限公司 | Wind speed and power prediction method, system, equipment and storage medium for wind power plant with complex terrain |
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