CN108629056A - A kind of wind power forecasting method and system - Google Patents
A kind of wind power forecasting method and system Download PDFInfo
- Publication number
- CN108629056A CN108629056A CN201710159229.3A CN201710159229A CN108629056A CN 108629056 A CN108629056 A CN 108629056A CN 201710159229 A CN201710159229 A CN 201710159229A CN 108629056 A CN108629056 A CN 108629056A
- Authority
- CN
- China
- Prior art keywords
- flow field
- wind
- prediction
- atmospheric flow
- wind speed
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000013277 forecasting method Methods 0.000 title abstract description 9
- 230000033001 locomotion Effects 0.000 claims abstract description 31
- 238000005259 measurement Methods 0.000 claims abstract description 21
- 238000013459 approach Methods 0.000 claims abstract description 9
- 238000000034 method Methods 0.000 claims description 46
- 238000013178 mathematical model Methods 0.000 claims description 24
- 239000012530 fluid Substances 0.000 claims description 18
- 238000012360 testing method Methods 0.000 claims description 12
- 230000005540 biological transmission Effects 0.000 claims description 11
- 238000004364 calculation method Methods 0.000 claims description 7
- 238000006243 chemical reaction Methods 0.000 claims description 7
- 238000004134 energy conservation Methods 0.000 claims description 6
- 238000005096 rolling process Methods 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 6
- 230000008859 change Effects 0.000 claims description 5
- 238000010276 construction Methods 0.000 claims description 5
- 230000007613 environmental effect Effects 0.000 claims description 5
- 239000000284 extract Substances 0.000 claims description 4
- 238000002715 modification method Methods 0.000 claims description 3
- 235000013399 edible fruits Nutrition 0.000 claims 1
- 230000035772 mutation Effects 0.000 abstract description 4
- 230000002123 temporal effect Effects 0.000 description 14
- 230000008569 process Effects 0.000 description 6
- 230000005684 electric field Effects 0.000 description 3
- 230000005611 electricity Effects 0.000 description 3
- 238000004088 simulation Methods 0.000 description 3
- 230000007812 deficiency Effects 0.000 description 2
- 238000011161 development Methods 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000012545 processing Methods 0.000 description 2
- 238000011144 upstream manufacturing Methods 0.000 description 2
- 238000013316 zoning Methods 0.000 description 2
- 238000004458 analytical method Methods 0.000 description 1
- 238000013473 artificial intelligence Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 238000004422 calculation algorithm Methods 0.000 description 1
- 238000000205 computational method Methods 0.000 description 1
- 238000005516 engineering process Methods 0.000 description 1
- 238000009415 formwork Methods 0.000 description 1
- 230000010354 integration Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000000053 physical method Methods 0.000 description 1
- 238000010248 power generation Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000001172 regenerating effect Effects 0.000 description 1
- 230000006641 stabilisation Effects 0.000 description 1
- 238000011105 stabilization Methods 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000012706 support-vector machine Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E60/00—Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- General Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Geometry (AREA)
- Entrepreneurship & Innovation (AREA)
- Game Theory and Decision Science (AREA)
- Evolutionary Computation (AREA)
- Quality & Reliability (AREA)
- Development Economics (AREA)
- Computer Hardware Design (AREA)
- General Engineering & Computer Science (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Wind Motors (AREA)
- Management, Administration, Business Operations System, And Electronic Commerce (AREA)
Abstract
The present invention relates to a kind of wind power forecasting method and system, the atmospheric flow field distribution of current time or historical juncture can be obtained in the atmospheric flow field model that the actual measurement physical quantity in flow field at discrete measuring point is established as primary condition, input using CFD approach；On this basis, the distribution of future time instance atmospheric flow field can be obtained in the physical motion in CFD model simulated atmosphere flow field, to realize ultra-short term wind speed/power prediction.Technical solution provided by the invention simulates the physical motion of atmospheric flow field, can effectively improve super short-period wind power precision of prediction, especially effectively can capture and predict the power swing caused by wind speed mutation.
Description
Technical field
The present invention relates to a kind of wind power forecasting methods, and in particular to a kind of wind based on atmospheric flow field temporal correlation
Electrical power prediction technique and system.
Background technology
As clean regenerative resource, development is conducive to alleviate global energy crisis and environmental problem wind-power electricity generation,
Become new development trend in recent years.Different from normal power supplies power generation, Power Output for Wind Power Field relies on weather condition, has fluctuation
Property and intermittent feature.A large amount of wind power integration power grids bring greater impact the safe and stable operation of power grid.To wind power plant
Output power is predicted, wind power is included in the operation plan of power grid, is advantageously ensured that power quality, is reduced spare appearance
Amount reduces Operation of Electric Systems cost, is one of the important measures for ensureing the stabilization of power grids, economical operation.
Wind power prediction can be divided into short-term forecast and exceed the time limit to predict two classes：It is about several days following that short-term forecast is conceived to prediction
Wind power, be generally basede on numerical weather forecast progress；Ultra-short term prediction is conceived to the wind-powered electricity generation of prediction future about 15min-4h
Power, numerical weather forecast cannot be satisfied needs, be generally basede on historical power, (include artificial intelligence side using statistics class method
Method).
Using the super short-period wind power prediction technique of statistics class method, due to that can not consider the physics of atmospheric flow field movement
Process, precision of prediction is limited, can not especially capture the power rapid fluctuations caused by wind speed mutation, thus cannot be satisfied electricity
The needs of net real-time control.
At present, it is thus proposed that the wind power forecasting method based on wind-resources/output of wind electric field temporal correlation, but its side
Method is embodied in the wind turbine method that represents, neural network, support vector machines method, and essence is still statistical method, is not excavated
With the physics law using atmospheric flow field movement.It still can not thus overcome precision of prediction deficiency, cannot be satisfied requirement of engineering
Problem.
Invention content
To solve above-mentioned deficiency of the prior art, the object of the present invention is to provide one kind being based on atmospheric flow field temporal and spatial correlations
The wind power forecasting method and system, the present invention of property can effectively improve precision of prediction.
The purpose of the present invention is what is realized using following technical proposals：
The present invention provides a kind of super short-period wind power prediction technique, it is improved in that the method includes：
Establish wind measurement network acquisition meteorological data；
High-performance data transmission process system is established, to transmit and handle meteorological data；
Atmospheric flow field mathematical model is established by fluid dynamics CFD approach；
It is moved using CFD model simulated atmosphere flow field, prediction of wind speed；
Convert forecasting wind speed to power prediction.
Further, described to establish atmospheric flow field mathematical model, including：
Basic Differential governing equation and corresponding definite condition according to relationship between related physical quantity in atmospheric flow field are built
Formwork erection intends the mathematical model of atmospheric flow field；
Mathematical model based on atmospheric flow field calculates the atmospheric flow field of target area using numerical discretization method；
The calculating parameter for setting atmospheric flow field, until mathematical model output result and the test number in the simulated atmosphere flow field
According to consistent.
Further, the related physical quantity, including：Reflect wind speed, wind direction, temperature, pressure in atmospheric flow field；
The Basic Differential governing equation includes：Mass-conservation equation, momentum conservation equation, energy conservation equation；
The definite condition includes：Primary condition and boundary condition；
The numerical discretization method includes：Finite difference calculus, FInite Element or finite volume method.
Further, the calculating parameter of the setting atmospheric flow field, until the mathematical model in the simulated atmosphere flow field is defeated
It is consistent with test data to go out result, including：Tested and tested using real data, according to test result to calculating parameter into
Performing check and amendment；The modification method includes：It calculates and joins for dynamic viscosity, the heat transfer coefficient of fluid and viscous dissipation item
Number adjusts the size of numerical value one by one, until the mathematical model output result in simulated atmosphere flow field is consistent with test data, meter
The value range for calculating parameter is related with environmental condition.
Further, the calculating parameter includes：Mesh generation, primary condition, boundary condition and control parameter；Wherein：
Mesh generation：The grid is cube grid, and grid distance is between 100m-10km；
Primary condition：It is obtained according to anemometer tower measured data；
Boundary condition：Hypsography, ground vegetation and whether there is building；
Control parameter：Including dynamic viscosity, the heat transfer coefficient of fluid and viscous dissipation item.
Further, described to be moved using CFD model simulated atmosphere flow field, prediction of wind speed, including：
Using the collected actual measurement meteorological data of wind measurement network as primary condition, input in established CFD model；
Start CFD to calculate, the motion state in simulated atmosphere flow field obtains the object that subsequent time atmospheric flow field includes wind speed
The distribution of reason amount；
Rolling calculation obtains the distribution that atmospheric flow field in future time section includes the physical quantity of wind speed, realizes to future
The prediction of atmospheric flow field motion state extracts wind at the position based on prediction result according to the coordinate information of wind turbine/wind power plant
The time sequential value of speed variation, obtains the forecasting wind speed result of target location；The prediction result is a three-dimensional data, described
Three-dimensional data is respectively：Longitude, latitude and time, and the three-dimensional data is converted into one-dimensional data, obtain the wind turbine/wind
The wind speed time series data of electric field.
Further, described to convert forecasting wind speed to power prediction, including：Atmospheric flow field is predicted using power curve
As a result the air speed value in is converted into the power generating value of wind turbine/wind power plant, obtains wind power prediction.
The present invention also provides a kind of super short-period wind power forecasting systems, it is improved in that the system comprises：
Grid builds module：Meteorological data is acquired for establishing wind measurement network；
System builds module：For establishing high-performance data transmission process system, to transmit and handle meteorological data；
Model construction module：For establishing fluid dynamics CFD model；
Prediction module：For using the movement of CFD model simulated atmosphere flow field, prediction of wind speed；
Conversion module：For converting forecasting wind speed to power prediction.
Further, the structure module, further comprises：
Mathematical model builds module：For the Basic Differential controlling party according to relationship between related physical quantity in atmospheric flow field
Journey and corresponding definite condition establish the mathematical model in simulated atmosphere flow field；
Computing module：Atmospheric flow field for calculating target area using numerical discretization method；
Setting module：Calculating parameter for setting atmospheric flow field, until the mathematical model in the simulated atmosphere flow field is defeated
It is consistent with test data to go out result.
Further, the prediction module, further comprises：Using the collected actual measurement meteorological data of wind measurement network as just
Beginning condition inputs in established CFD model；
Start CFD to calculate, the motion state in simulated atmosphere flow field obtains the object that subsequent time atmospheric flow field includes wind speed
The distribution of reason amount；
Rolling calculation obtains the distribution that atmospheric flow field in future time section includes the physical quantity of wind speed, realizes to future
The prediction of atmospheric flow field motion state extracts wind at the position based on prediction result according to the coordinate information of wind turbine/wind power plant
The time sequential value of speed variation, obtains the forecasting wind speed result of target location；The prediction result is a three-dimensional data, described
Three-dimensional data is respectively：Longitude, latitude and time, and the three-dimensional data is converted into one-dimensional data, obtain the wind turbine/wind
The wind speed time series data of electric field；
The conversion module, further comprises：The air speed value in atmospheric flow field prediction result is converted using power curve
For the power generating value of wind turbine/wind power plant, wind power prediction result is obtained.
Compared with the immediate prior art, the excellent effect that technical solution provided by the invention has is：
The present invention uses hydrodynamic method (CFD) simulated atmosphere flow field change, starts with from physical mechanism, can be effective
Complicated atmospheric flow field time-space relationship is disclosed, the atmospheric flow field motion state of real-time change is described.
The present invention is based on atmospheric flow field temporal correlations can be obtained using CFD model according to the measured data of discrete measuring point
To the atmospheric flow field distribution for covering entire zoning.
Compared with traditional statistics class method, the physical motion in this method simulated atmosphere flow field can effectively improve ultra-short term
Wind power prediction precision especially effectively can capture and predict the power swing caused by wind speed mutation.
This method strong applicability can be suitable for various orographic conditions, various weather conditions.
Description of the drawings
Fig. 1 is the flow chart of the wind power forecasting method provided by the invention based on atmospheric flow field temporal correlation；
Fig. 2 is the structure chart of the wind power forecasting system provided by the invention based on atmospheric flow field temporal correlation.
Specific implementation mode
The specific implementation mode of the present invention is described in further detail below in conjunction with the accompanying drawings.
The following description and drawings fully show specific embodiments of the present invention, to enable those skilled in the art to
Put into practice them.Other embodiments may include structure, logic, it is electrical, process and other change.Embodiment
Only represent possible variation.Unless explicitly requested, otherwise individual component and function are optional, and the sequence operated can be with
Variation.The part of some embodiments and feature can be included in or replace part and the feature of other embodiments.This hair
The range of bright embodiment includes equivalent obtained by the entire scope of claims and all of claims
Object.Herein, these embodiments of the invention can individually or generally be indicated that this is only with term " invention "
For convenience, it and if in fact disclosing the invention more than one, is not meant to automatically limit ranging from appointing for the application
What single invention or inventive concept.
Embodiment one,
Field data show, there are temporal correlation, one of performance is that wind speed variation exists centainly for the variation of atmospheric flow field
Spatial coherence and time-lag effect, if wind speed increase/reduction of upwind anemometer tower is earlier than lower wind direction anemometer tower, the characteristic
It can be applied to the ultra-short term prediction of wind speed/wind power.However, the performance of temporal correlation is complex, such as upstream and downstream wind
Speed variation has positive correlation, inversely related, other correlations etc., this is because by the shadow of the factors such as flow field characteristic, landform, roughness of ground surface
Ring, the physical motion process of atmospheric flow field is complex, statistics class method can not disclose its inner link, it is necessary to using precision compared with
High physical method could effectively simulated atmosphere flow field motion process, the inherent law of announcement upstream and downstream wind speed variation improve super
Short-term wind speed/power prediction precision.
Based on this thinking, the present invention uses CFD approach, proposes a kind of ultra-short term based on atmospheric flow field temporal correlation
Wind power forecasting method.Compared with traditional statistics class method, this method simulates the physical motion of atmospheric flow field, can be effective
Super short-period wind power precision of prediction is improved, especially effectively can capture and predict the power swing caused by wind speed mutation.
Flow chart such as Fig. 1 institutes of wind power forecasting method provided by the invention based on atmospheric flow field temporal correlation
Show, includes the following steps：
(1) wind measurement network is formed
Centered on wind power plant or target prediction region, built or newly-built multiple wind measuring devices, shape are chosen in neighboring area
At wind measurement network.Wind measuring device can be anemometer tower, can also be laser anemometer etc..Wind measuring device should be able to measure multiple layers
The conventional meteorological data such as high wind speed, wind direction and temperature, pressure.
Wind measuring device position distribution principle is as follows：
1. wind measuring device should be between 10~200km at a distance from prediction target area；
2. wind measuring device is no less than 2, quantity is The more the better；
3. wind measuring device is uniformly distributed as far as possible, or can represent regional wind regime comprehensively, such as different terrain conditions, no
With wind measuring device should all be equipped under the conditions of roughness of ground surface；
4. if region is there are cardinal wind, then upwind position can suitably increase wind measuring device.
Each sensor of wind measuring device should all pass through calibration, ensure the reliability of acquisition and transmission data.
(2) high-performance data transmission, processing system are formed
High-performance data transmission, processing system are built, system includes center processor and data transmission network.Data transmission network
The real-time data transmission that each anemometer tower can be acquired is to center processor；Center processor can store mass data, into
Row high speed, high-precision fluid dynamics numerical computations.
(3) CFD approach is used, atmospheric flow field motion model is established：
For the atmospheric flow field characteristic of zoning, numerical model is established using computational fluid dynamics (CFD) method, is wrapped
Containing following steps：
(a) mathematical model in simulated atmosphere flow field is established.Specifically be exactly establish reflection atmospheric flow field in wind speed, wind direction,
The differential equation of relationship and corresponding definite condition, Basic Differential governing equation generally include between the physical quantitys such as temperature, pressure
Mass-conservation equation, momentum conservation equation, energy conservation equation and the corresponding definite condition of these equations (primary condition and
Boundary condition).
Mass-conservation equation：
In formula, ρ is density, and t is the time, and u, v and w are components of the velocity vector u in the direction x, y and z.
Momentum conservation equation (is suitable for Newtonian fluid)：
In formula, μ is dynamic viscosity, and p is pressure；Su、SvAnd SwIt is broad sense source item, Other parameters are same as above.
Energy conservation equation：
In formula, cpIt is specific heat capacity, T is temperature, and k is the heat transfer coefficient of fluid, STFor viscous dissipation item, other parameters are same as above.
The Ideal-Gas Equation：
P=ρ RT (4)
In formula, R is mol gas constant, and other parameters are same as above.
Mass-conservation equation, momentum conservation equation, energy conservation equation are collectively referred to as fundamental equation, and 6 are shared in fundamental equation
Unknown quantity u, v, w, p, T, ρ (other parameters are physical constant).Fundamental equation forms equation group, side with The Ideal-Gas Equation
Journey group is made of 6 independent equations, thus equation group closing can solve.
(b) computational methods of suitable target area atmospheric flow field are determined.Establish the numerical discretization for governing equation
Method, such as finite difference calculus, FInite Element, finite volume method.
(c) factorization, setup algorithm parameter.Including calculating the defeated of mesh generation, primary condition and boundary condition
Enter, the setting etc. of control parameter.It is tested and is tested using real data, tested to calculating parameter according to test result
With amendment.Modification method：For calculating parameter (dynamic viscosity, the biography of fluid in formula (2a), (2b), (2c) and formula (3)
Hot coefficient, viscous dissipation item etc.), the size of numerical value is adjusted one by one, until model output result is consistent with test data.Respectively
The value range of parameter is related with environmental condition.
Mesh generation：The grid is cube grid, and grid distance is between 100m-10km；
If computational accuracy requires the high and high mesh generation of computer performance thinner, otherwise mesh generation is thicker.
Primary condition：That is air speed data is obtained according to anemometer tower measured data；
Boundary condition：Refer to hypsography, ground vegetation and whether has building；
Control parameter, including dynamic viscosity, it is related with environmental condition.
So far, just have the temporal correlation according to atmospheric flow field, the item of wind power prediction is carried out using CFD approach
Part.
(4) simulated atmosphere flow field motion process realizes forecasting wind speed
It is used as primary condition, input established the collected actual measurement meteorological data of wind measurement network (wind speed, wind direction etc.)
Atmospheric flow field motion model.Start CFD numerical computations, subsequent time (15min) can be obtained in the motion state in simulated atmosphere flow field
The distribution of each physical quantity (wind speed, wind direction etc.) in atmospheric flow field；It is big to obtain in future time section (15min~4h) for rolling calculation
Airflow field includes the distribution of the physical quantity of wind speed, realizes the prediction to the following atmospheric flow field motion state.
It is extracted at wind turbine/wind power plant position according to the coordinate information of wind turbine/wind power plant based on atmospheric flow field prediction result
The time sequential value of wind speed variation, you can obtain the forecasting wind speed result of target location.
(5) forecasting wind speed result is converted to power：
Based on forecasting wind speed as a result, using the methods of power curve, air speed value can be converted to the output of wind turbine/wind power plant
Value, to obtain wind power prediction result.Specifically：It is carried according to the coordinate information of wind turbine/wind power plant based on prediction result
The time sequential value that wind speed changes at the position is taken, the forecasting wind speed result of target location is obtained；The prediction result is one
Three-dimensional data, the three-dimensional data are respectively：Longitude, latitude and time, and the three-dimensional data is converted into one-dimensional data, it obtains
To the wind speed time series data of the wind turbine/wind power plant.
The present invention establishes atmospheric flow field motion model using computational fluid dynamics method.Computational fluid dynamics
(Computational Fluid Dynamics, abbreviation CFD) is shown by computer numerical value calculation and image, to including
The analysis that fluid flows and the system of the relevant physical phenomenas such as heat transfer is done.CFD approach is that (quality is kept in fundamental flowing equations
Permanent equation, momentum conservation equation, energy conservation equation) under control to the numerical simulation of flowing.By this numerical simulation, we
Can obtain the fundamental physical quantity (such as speed, pressure, temperature, concentration) in flow field on each position distribution and these
Physical quantity changes with time situation.
Since atmospheric flow field has temporal correlation, thus can be by actual measurement physical quantity (such as wind in flow field at discrete measuring point
The data such as speed, wind direction) it is used as primary condition, the atmospheric flow field model that input is established using CFD approach that current time can be obtained
The distribution of (historical juncture) atmospheric flow field；On this basis, the physical motion in CFD model simulated atmosphere flow field can be obtained
The distribution of future time instance atmospheric flow field, to realize ultra-short term wind speed/power prediction.
Embodiment two,
Based on same inventive concept, the present invention also provides a kind of wind power based on atmospheric flow field temporal correlation is pre-
Examining system, structure chart is as shown in Fig. 2, include：
Grid builds module 201：Meteorological data is acquired for establishing wind measurement network；
System builds module 202：For establishing high-performance data transmission process system, to transmit and handle meteorological number
According to；
Model construction module 203：For using fluid dynamics CFD founding mathematical models；
Prediction module 204：For using the movement of CFD model simulated atmosphere flow field, prediction of wind speed；
Conversion module 205：For converting forecasting wind speed to power prediction.
Model construction module 203, further comprises：
Mathematical model builds module：Mathematical model for establishing simulated atmosphere flow field；
Computing module：Big air-flow for calculating target area using finite difference calculus, FInite Element or finite volume method
；
Setting module：Calculating parameter for setting atmospheric flow field.
Prediction module 204, further comprises：It is defeated using the collected actual measurement meteorological data of wind measurement network as primary condition
Enter in established CFD model；
Start CFD to calculate, the motion state in simulated atmosphere flow field obtains wind speed in subsequent time (15min) atmospheric flow field
Etc. physical quantitys distribution；
Rolling calculation obtains in following a period of time the distribution of each physical quantity in (15min-4h) atmospheric flow field, to real
Now to the prediction of atmospheric flow field motion state.Based on prediction result, according to the coordinate information of wind turbine/wind power plant, the position is extracted
The time sequential value for locating wind speed variation, obtains the forecasting wind speed result of target location；The prediction result is a three-dimensional data,
The three-dimensional data is respectively：Longitude, latitude and time, and the three-dimensional data is converted into one-dimensional data through conversion, it obtains
The wind speed time series data of the wind turbine/wind power plant.
Conversion module 205, further comprises：The air speed value in atmospheric flow field prediction result is converted to using power curve
The power generating value of wind turbine/wind power plant obtains wind power prediction result.
The present invention is based on the simulations to atmospheric flow field motion process, provide a kind of super based on atmospheric flow field temporal correlation
Short-term wind power forecast method can effectively improve precision of prediction.
The above embodiments are merely illustrative of the technical scheme of the present invention and are not intended to be limiting thereof, although with reference to above-described embodiment pair
The present invention is described in detail, those of ordinary skill in the art still can to the present invention specific implementation mode into
Row modification either equivalent replacement these without departing from any modification of spirit and scope of the invention or equivalent replacement, applying
Within the claims of the pending present invention.
Claims (10)
1. a kind of super short-period wind power prediction technique, which is characterized in that the method includes：
Establish wind measurement network acquisition meteorological data；
High-performance data transmission process system is established, to transmit and handle meteorological data；
Atmospheric flow field mathematical model is established by fluid dynamics CFD approach；
The atmospheric flow field mathematical model simulated atmosphere flow field movement established using CFD approach, prediction of wind speed；
Convert forecasting wind speed to power prediction.
2. super short-period wind power prediction technique as described in claim 1, which is characterized in that described to establish atmospheric flow field mathematics
Model, including：
Basic Differential governing equation and corresponding definite condition according to relationship between related physical quantity in atmospheric flow field establish mould
The mathematical model of quasi- atmospheric flow field；
Mathematical model based on atmospheric flow field calculates the atmospheric flow field of target area using numerical discretization method；
The calculating parameter for setting atmospheric flow field, until mathematical model output result and the test data one in the simulated atmosphere flow field
It causes.
3. super short-period wind power prediction technique as claimed in claim 2, which is characterized in that the related physical quantity, including：
Reflect wind speed, wind direction, temperature, pressure in atmospheric flow field；
The Basic Differential governing equation includes：Mass-conservation equation, momentum conservation equation, energy conservation equation；
The definite condition includes：Primary condition and boundary condition；
The numerical discretization method includes：Finite difference calculus, FInite Element or finite volume method.
4. super short-period wind power prediction technique as claimed in claim 2, which is characterized in that the meter of the setting atmospheric flow field
Calculate parameter, until the simulated atmosphere flow field mathematical model output result it is consistent with test data, including：Using real data
It is tested and is tested, calculating parameter is tested and corrected according to test result；The modification method includes：For power
Viscosity, the heat transfer coefficient of fluid and viscous dissipation item calculating parameter adjust the size of numerical value, until simulated atmosphere flow field one by one
Until mathematical model output result is consistent with test data, the value range of calculating parameter is related with environmental condition.
5. super short-period wind power prediction technique as claimed in claim 2, which is characterized in that the calculating parameter includes：Net
Lattice division, primary condition, boundary condition and control parameter；Wherein：
Mesh generation：The grid is cube grid, and grid distance is between 100m-10km；
Primary condition：Air speed data is obtained according to anemometer tower measured data；
Boundary condition：Hypsography, ground vegetation and whether there is building；
Control parameter：Including dynamic viscosity, the heat transfer coefficient of fluid and viscous dissipation item.
6. super short-period wind power prediction technique as described in claim 1, which is characterized in that described to be simulated using CFD model
Atmospheric flow field moves, prediction of wind speed, including：
Using the collected actual measurement meteorological data of wind measurement network as primary condition, input in established CFD model；
Start CFD to calculate, the motion state in simulated atmosphere flow field obtains the physical quantity that subsequent time atmospheric flow field includes wind speed
Distribution；
Rolling calculation obtains the distribution that atmospheric flow field in future time section includes the physical quantity of wind speed, realizes to the following air
The prediction of flow field motion state is based on prediction result, according to the coordinate information of wind turbine/wind power plant, extracts wind speed at the position and becomes
The time sequential value of change obtains the forecasting wind speed result of target location；The prediction result is a three-dimensional data, the three-dimensional
Data are respectively：Longitude, latitude and time, and the three-dimensional data is converted into one-dimensional data, obtain the wind turbine/wind power plant
Wind speed time series data.
7. super short-period wind power prediction technique as described in claim 1, which is characterized in that described to convert forecasting wind speed to
Power prediction, including：Wind speed time series data in atmospheric flow field prediction result is converted to by air speed value using power curve
It is converted into the power generating value of wind turbine/wind power plant, obtains wind power prediction.
8. a kind of super short-period wind power forecasting system, which is characterized in that the system comprises：
Grid builds module：Meteorological data is acquired for establishing wind measurement network；
System builds module：For establishing high-performance data transmission process system, to transmit and handle meteorological data；
Model construction module：For establishing fluid dynamics CFD model；
Prediction module：For using the movement of CFD model simulated atmosphere flow field, prediction of wind speed；
Conversion module：For converting forecasting wind speed to power prediction.
9. super short-period wind power forecasting system as claimed in claim 8, which is characterized in that the model construction module, into
One step includes：
Mathematical model builds module：For according to the Basic Differential governing equation of relationship between related physical quantity in atmospheric flow field and
Corresponding definite condition establishes the mathematical model in simulated atmosphere flow field；
Computing module：Atmospheric flow field for calculating target area using numerical discretization method；
Setting module：Calculating parameter for setting atmospheric flow field, until the mathematical model in the simulated atmosphere flow field exports knot
Fruit is consistent with test data.
10. super short-period wind power forecasting system as claimed in claim 8, which is characterized in that the prediction module, further
Including：Using the collected actual measurement meteorological data of wind measurement network as primary condition, input in established CFD model；
Start CFD to calculate, the motion state in simulated atmosphere flow field obtains the physical quantity that subsequent time atmospheric flow field includes wind speed
Distribution；
Rolling calculation obtains the distribution that atmospheric flow field in future time section includes the physical quantity of wind speed, realizes to the following air
The prediction of flow field motion state is based on prediction result, according to the coordinate information of wind turbine/wind power plant, extracts wind speed at the position and becomes
The time sequential value of change obtains the forecasting wind speed result of target location；The prediction result is a three-dimensional data, the three-dimensional
Data are respectively：Longitude, latitude and time, and the three-dimensional data is converted into one-dimensional data, obtain the wind turbine/wind power plant
Wind speed time series data；
The conversion module, further comprises：Air speed value in atmospheric flow field prediction result is converted by wind using power curve
The power generating value of machine/wind power plant obtains wind power prediction result.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710159229.3A CN108629056A (en) | 2017-03-17 | 2017-03-17 | A kind of wind power forecasting method and system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710159229.3A CN108629056A (en) | 2017-03-17 | 2017-03-17 | A kind of wind power forecasting method and system |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN108629056A true CN108629056A (en) | 2018-10-09 |
Family
ID=63686787
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710159229.3A Pending CN108629056A (en) | 2017-03-17 | 2017-03-17 | A kind of wind power forecasting method and system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN108629056A (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109978275A (en) * | 2019-04-03 | 2019-07-05 | 中南大学 | A kind of extreme wind wind speed forecasting method and system mixing CFD and deep learning |
| CN112348292A (en) * | 2021-01-07 | 2021-02-09 | 中国电力科学研究院有限公司 | Short-term wind power prediction method and system based on deep learning network |
| CN113780690A (en) * | 2021-11-12 | 2021-12-10 | 南京亚流航空科技有限公司 | Environmental risk assessment system and method based on CFD software technology |
| CN113985762A (en) * | 2021-10-08 | 2022-01-28 | 北京华能新锐控制技术有限公司 | Wind power generation airflow monitoring system |
| CN115081742A (en) * | 2022-07-22 | 2022-09-20 | 北京东润环能科技股份有限公司 | Ultra-short-term power prediction method for distributed wind power plant and related equipment |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102055188A (en) * | 2011-01-07 | 2011-05-11 | 西北电网有限公司 | Ultra-short term wind power forecasting method based on time series method |
| CN102768738A (en) * | 2012-07-31 | 2012-11-07 | 华锐风电科技(集团)股份有限公司 | Method and device for power predication of wind power station |
| CN104699936A (en) * | 2014-08-18 | 2015-06-10 | 沈阳工业大学 | Sector management method based on CFD short-term wind speed forecasting wind power plant |
-
2017
- 2017-03-17 CN CN201710159229.3A patent/CN108629056A/en active Pending
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102055188A (en) * | 2011-01-07 | 2011-05-11 | 西北电网有限公司 | Ultra-short term wind power forecasting method based on time series method |
| CN102768738A (en) * | 2012-07-31 | 2012-11-07 | 华锐风电科技(集团)股份有限公司 | Method and device for power predication of wind power station |
| CN104699936A (en) * | 2014-08-18 | 2015-06-10 | 沈阳工业大学 | Sector management method based on CFD short-term wind speed forecasting wind power plant |
Non-Patent Citations (5)
| Title |
|---|
| G. KARINIOTAKIS等: "The State of the Art in Short-term Prediction of Wind Power - From an Offshore Perspective", 《PROC. OF 2004 SEATECHWEEK》 * |
| 刘永前等: "基于CFD流场预计算的风电功率预测误差分布研究", 《现代电力》 * |
| 国家安全生产监督监理总结规划科技司等: "《"十五"国家安全生产优秀科技成果汇编："十五"国家科技攻关计划科研成果（煤矿分册）》", 30 June 2007, 煤炭工业出版社 * |
| 崔嘉: "分散式风电场功率预测与控制研究", 《中国博士学位论文全文数据库 工程科技Ⅱ辑》 * |
| 王一妹: "基于CFD流场预计算的复杂地形风电场功率预测方法研究", 《中国优秀硕士学位论文全文数据库 工程科技Ⅱ辑》 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109978275A (en) * | 2019-04-03 | 2019-07-05 | 中南大学 | A kind of extreme wind wind speed forecasting method and system mixing CFD and deep learning |
| CN109978275B (en) * | 2019-04-03 | 2021-03-12 | 中南大学 | Extreme strong wind speed prediction method and system based on mixed CFD and deep learning |
| CN112348292A (en) * | 2021-01-07 | 2021-02-09 | 中国电力科学研究院有限公司 | Short-term wind power prediction method and system based on deep learning network |
| CN112348292B (en) * | 2021-01-07 | 2021-08-06 | 中国电力科学研究院有限公司 | Short-term wind power prediction method and system based on deep learning network |
| CN113985762A (en) * | 2021-10-08 | 2022-01-28 | 北京华能新锐控制技术有限公司 | Wind power generation airflow monitoring system |
| CN113985762B (en) * | 2021-10-08 | 2023-08-08 | 北京华能新锐控制技术有限公司 | Wind power generation air current monitored control system |
| CN113780690A (en) * | 2021-11-12 | 2021-12-10 | 南京亚流航空科技有限公司 | Environmental risk assessment system and method based on CFD software technology |
| CN113780690B (en) * | 2021-11-12 | 2022-02-15 | 南京亚流航空科技有限公司 | Environmental risk assessment system and method based on CFD software technology |
| CN115081742A (en) * | 2022-07-22 | 2022-09-20 | 北京东润环能科技股份有限公司 | Ultra-short-term power prediction method for distributed wind power plant and related equipment |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN108629056A (en) | A kind of wind power forecasting method and system | |
| CN106326625B (en) | A kind of simulation of wind method coupling WRF and OpenFOAM mode | |
| CN103279804B (en) | The Forecasting Methodology of super short-period wind power | |
| CN102945508B (en) | Model correction based wind power forecasting method | |
| Lee et al. | Observing and simulating wind-turbine wakes during the evening transition | |
| CN106505631B (en) | Intelligent wind power wind power prediction system | |
| Castellani et al. | Wind power forecasting techniques in complex terrain: ANN vs. ANN-CFD hybrid approach | |
| CN103116711B (en) | Weather pattern discrimination method based on irradiance characteristic parameter | |
| CN101794996A (en) | Real-time predicting method for output of wind electric field | |
| Jin et al. | Equivalent modeling of wind energy conversion considering overall effect of pitch angle controllers in wind farm | |
| CN103617308B (en) | A kind of construction method of wind power plant frequency domain equivalent model | |
| Guo et al. | VIC distributed hydrological model to predict climate change impact in the Hanjiang basin | |
| CN106779208A (en) | A kind of wind-powered electricity generation ultra-short term power forecasting method based on virtual anemometer tower technology | |
| Yuan et al. | Coupled wind farm parameterization with a mesoscale model for simulations of an onshore wind farm | |
| Li et al. | Wind tunnel study on the morphological parameterization of building non-uniformity | |
| Lodge et al. | Short term wind speed prediction using artificial neural networks | |
| CN108205713A (en) | A kind of region wind power prediction error distribution determination method and device | |
| CN107358059A (en) | Short-term photovoltaic energy Forecasting Methodology and device | |
| Al-Quraan et al. | Use of a wind tunnel for urban wind power estimation | |
| CN117233866A (en) | CNOP-based weather set forecasting system, method and storage medium | |
| CN112231979B (en) | Mountain area instantaneous wind condition forecasting method based on computational fluid dynamics and machine learning | |
| CN105718695A (en) | Wind field reconstruction method based on compressive sensing | |
| Laib et al. | Spatial modelling of extreme wind speed distributions in Switzerland | |
| Guo | A simple method to downscale daily wind statistics to hourly wind data | |
| Özgür | ANN-based evaluation of wind power generation: A case study in Kutahya, Turkey |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |