CN115544907A - Wind power prediction and stock wind power optimization control method based on wake effect - Google Patents
Wind power prediction and stock wind power optimization control method based on wake effect Download PDFInfo
- Publication number
- CN115544907A CN115544907A CN202211142794.6A CN202211142794A CN115544907A CN 115544907 A CN115544907 A CN 115544907A CN 202211142794 A CN202211142794 A CN 202211142794A CN 115544907 A CN115544907 A CN 115544907A
- Authority
- CN
- China
- Prior art keywords
- wind
- wind power
- power
- data
- wind turbine
- 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
Images
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
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/004—Generation forecast, e.g. methods or systems for forecasting future energy generation
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J3/00—Circuit arrangements for ac mains or ac distribution networks
- H02J3/38—Arrangements for parallely feeding a single network by two or more generators, converters or transformers
- H02J3/46—Controlling of the sharing of output between the generators, converters, or transformers
- H02J3/466—Scheduling the operation of the generators, e.g. connecting or disconnecting generators to meet a given demand
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/04—Power grid distribution networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/06—Wind turbines or wind farms
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2207/00—Indexing scheme relating to details of circuit arrangements for charging or depolarising batteries or for supplying loads from batteries
-
- H—ELECTRICITY
- H02—GENERATION; CONVERSION OR DISTRIBUTION OF ELECTRIC POWER
- H02J—CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY
- H02J2300/00—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation
- H02J2300/40—Systems for supplying or distributing electric power characterised by decentralized, dispersed, or local generation wherein a plurality of decentralised, dispersed or local energy generation technologies are operated simultaneously
-
- 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
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- General Engineering & Computer Science (AREA)
- Computational Mathematics (AREA)
- Algebra (AREA)
- Power Engineering (AREA)
- Operations Research (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Bioinformatics & Computational Biology (AREA)
- Databases & Information Systems (AREA)
- Software Systems (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Probability & Statistics with Applications (AREA)
- Computing Systems (AREA)
- Fluid Mechanics (AREA)
- Computer Hardware Design (AREA)
- Evolutionary Computation (AREA)
- Geometry (AREA)
- Wind Motors (AREA)
Abstract
The invention relates to the technical field of wind power prediction and stock wind power optimization, in particular to a method for predicting wind power and optimally controlling stock wind power based on wake effect, which comprises the following steps of S1: first, environmental factors and the thrust coefficient of the most upstream wind turbine are introduced, and the predicted power characteristics of the wind turbine are introduced. In the method, the operation function of single-column data is realized through steps 1-4, environmental factor data such as wind shear, turbulence intensity and the like, aerodynamic characteristics of wing profiles in blades, pitch angle and other dynamic characteristic data of each blade are added in the operation process, a Navier-Stokes equation for controlling the whole flow field in the wind power plant is numerically solved through a Jensen model, wind speed attenuation is calculated, and the accuracy of an operation result is improved.
Description
Technical Field
The invention relates to the technical field of wind power prediction and stock wind power optimization, in particular to a wind power prediction and stock wind power optimization control method based on a wake effect.
Background
The wind power prediction and stock wind power optimization are processes of calculating wind power and optimizing the wind power based on prediction of wake loss of a wind power plant in construction of the wind power plant, in the traditional wind power prediction, coefficients participating in calculation often only comprise wind speed coefficients, data such as wind shear and turbulence intensity are not subjected to relevant reference, so that deviation exists between a calculation result and actual input power, the predicted result is often calculation for single-column data, the calculation data of the whole wind power plant is not subjected to relevant sorting improvement, so that the redundancy of the whole calculation is large, and the calculation of the single-column data possibly conflicts with the operation of the whole wind power plant and needs to be improved.
Disclosure of Invention
The invention aims to solve the defects in the prior art, and provides a wind power prediction and stock wind power optimization control method based on a wake effect.
In order to achieve the purpose, the invention adopts the following technical scheme: the method for predicting wind power and optimally controlling wind power inventory based on wake effect comprises the following steps:
s1: firstly, introducing environmental factors and a thrust coefficient of a most upstream wind turbine, and introducing the predicted power characteristics of the wind turbine as an operation basic item;
s2: after the most upstream wind turbine is started, calculating output power through the thrust coefficient of the most upstream wind turbine;
s3: calculating the environmental factors of the position of the downstream wind turbine so as to calculate the output power of the downstream wind turbine;
s4: the operation result is changed into the output power of the upstream wind turbine, so that the environmental factors and the output power of the positions of the downstream wind turbines are calculated until the output power of the most downstream wind turbine is calculated, and the output power of the wind turbines at all positions is used as an extension operation item to derive an integral power model;
s5: in the implementation operation, firstly, data are collected and processed;
s6: a wind power prediction stage;
s7: and (4) performing statistical analysis and optimization.
Preferably, in S1, the environmental factors include wind speed, wind shear and turbulence intensity, and the dynamic characteristics of the wind turbine include the aerodynamic characteristics of an airfoil in each blade and the pitch angle of each blade.
Preferably, in S2, the calculating the output power by the thrust coefficient calculation includes:
s201: the method comprises the steps that the aerodynamic characteristics of an airfoil in a blade and the pitch angle of each blade are used as basic operation terms based on the dynamic characteristics of a wind turbine;
s202: calculating a basic thrust coefficient of the wind turbine by taking the starting rotating speed of the wind turbine as an input value;
s203: and (4) calculating the actual output power of the uppermost stream wind turbine during starting according to the environmental factors.
Preferably, in S3, the calculating the environmental factor of the downstream wind turbine position specifically includes calculating the wind speed, the wind shear, and the turbulence intensity of the downstream wind turbine position through a Jensen model.
Preferably, the calculating, by the Jensen model, the wind speed, the wind shear and the turbulence intensity at the downstream wind turbine position includes:
s301: based on WLMs of computational fluid mechanics, carrying out numerical solution on a Navier-Stokes equation for controlling the whole flow field in the wind power plant;
s302: the wind speed decay in the turbine wake is calculated assuming axisymmetric expansion downstream of the wake described by a constant wake decay constant (kW).
Preferably, in S5, the collected data includes numerical weather forecast data, real-time anemometer tower data, and operating states of the wind turbine generator and the wind farm.
Preferably, in S5, the processing of the data specifically includes importing numerical weather forecast data, real-time anemometer tower data, wind turbine generator and wind farm into the overall power model for operation, performing integrity and rationality check, correcting missing measurement and abnormal data, and finally storing the data in the database.
Preferably, in S6, the wind power prediction stage includes:
s601: predicting the output power of the wind power plant through the wind power of the wind power plant section;
s602: and predicting the wind power output power of a single wind power plant, a local control area and the whole scheduling jurisdiction area through the wind power of the power grid scheduling end.
Preferably, in S7, the statistical analysis includes data statistics, correlation verification, error statistics on a prediction result of any time interval, and error statistics on a prediction curve reported by each wind farm in different scheduling jurisdictions, the optimization specifically includes comparing whether the wind power rate is optimal under the environmental factor, if so, performing checking and execution, if not, adjusting a thrust coefficient of the wind turbine, and performing a loop operation until the optimal value is reached.
Preferably, the data statistics specifically include historical power data, wind measurement data, integrity, frequency distribution, change rate and the like of numerical weather forecast data, the wind power plant operation parameter statistics include parameter statistics such as generated energy, effective power generation time, maximum output, generation time, synchronization rate, utilization hours and average coincidence rate, the correlation verification specifically includes performing correlation verification on the historical power data, the wind measurement data and the numerical weather forecast data, and according to an analysis result, giving errors possibly introduced by uncertainty of the data, the error indexes for performing error statistics on a prediction result of any time interval include root mean square errors, average absolute errors and correlation coefficients, and the errors for performing statistics on prediction curves reported by the wind power plants in different scheduling jurisdiction ranges include assessment scores, electricity deductions and uploading rates.
Compared with the prior art, the invention has the advantages and positive effects that:
in the invention, the operation function of single-column data is realized through the steps 1-4, environmental factor data such as wind shear, turbulence intensity and the like, aerodynamic characteristics of wing profiles in blades, power characteristic data such as pitch angle of each blade and the like are added in the operation process, a Navier-Stokes equation for controlling the whole flow field in the wind power plant is numerically solved through a Jensen model, wind speed attenuation is calculated, and the accuracy of the operation result is improved.
Drawings
FIG. 1 is a schematic diagram of main steps of a wind power prediction and stock wind power optimization control method based on wake effect according to the present invention;
FIG. 2 is a detailed schematic diagram of step 2 of the method for predicting wind power and optimally controlling wind power storage amount based on wake effect according to the present invention;
FIG. 3 is a detailed schematic diagram of step 3 of the method for predicting wind power and optimally controlling wind power storage amount based on wake effect according to the present invention;
fig. 4 is a detailed schematic diagram of step 6 of the wind power prediction and stock wind power optimization control method based on the wake effect.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
In the description of the present invention, it is to be understood that the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are used only for convenience in describing the present invention and for simplicity in description, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed in a particular orientation, and be operated, and thus, are not to be construed as limiting the present invention. Further, in the description of the present invention, "a plurality" means two or more unless specifically defined otherwise.
Example one
Referring to fig. 1, the present invention provides a technical solution: the method for predicting wind power and optimally controlling wind power inventory based on wake effect comprises the following steps:
s1: firstly, introducing environmental factors and a thrust coefficient of a most upstream wind turbine, and introducing the predicted power characteristics of the wind turbine as an operation basic item;
s2: after the most upstream wind turbine is started, calculating output power through the thrust coefficient of the most upstream wind turbine;
s3: calculating the environmental factors of the position of the downstream wind turbine so as to calculate the output power of the downstream wind turbine;
s4: the operation result is changed into the output power of the upstream wind turbine, so that the environmental factors and the output power of the positions of the downstream wind turbines are calculated until the output power of the most downstream wind turbine is calculated, and the output power of the wind turbines at all positions is used as an extension operation item to derive an integral power model;
s5: in the implementation operation, firstly, data are collected and processed;
s6: a wind power prediction stage;
s7: and (4) carrying out statistical analysis and optimization.
Referring to fig. 1, in S1, the environmental factors include wind speed, wind shear and turbulence intensity, the dynamic characteristics of the wind turbine include the aerodynamic characteristics of the airfoil in the blade and the pitch angle of each blade, and in the calculation process, the environmental factor data such as wind shear and turbulence intensity, the aerodynamic characteristics of the airfoil in the blade and the dynamic characteristic data such as the pitch angle of each blade are added, so that the comprehensive characteristics of the calculation data are improved.
Referring to fig. 2, in S2, calculating the output power by calculating the thrust coefficient includes:
s201: the method comprises the steps that the aerodynamic characteristics of an airfoil in a blade and the pitch angle of each blade are used as basic operation terms based on the dynamic characteristics of a wind turbine;
s202: calculating a basic thrust coefficient of the wind turbine by taking the starting rotating speed of the wind turbine as an input value;
s203: and (4) calculating the actual output power of the uppermost stream wind turbine during starting according to the environmental factors.
The design is to refine the calculation of the output power through the calculation of the thrust coefficient, so as to realize the calculation function of the actual output power for starting the upstream wind turbine.
Referring to fig. 3, in S3, the calculating the environmental factors of the downstream wind turbine position specifically includes calculating the wind speed, the wind shear, and the turbulence intensity of the downstream wind turbine position by using a Jensen model, and the calculating the wind speed, the wind shear, and the turbulence intensity of the downstream wind turbine position by using the Jensen model includes:
s301: based on WLMs of computational fluid mechanics, carrying out numerical solution on a Navier-Stokes equation for controlling the whole flow field in the wind power plant;
s302: the wind speed decay in the turbine wake is calculated assuming axisymmetric expansion downstream of the wake described by a constant wake decay constant (kW).
The design is that the step of calculating the wind speed, the wind shear and the turbulence intensity of the downstream wind turbine position through a Jensen model is subjected to thinning processing, so that the calculation function of wind speed attenuation is achieved.
Referring to fig. 1, in S5, the collected data includes numerical weather forecast data, real-time anemometer tower data, wind turbine generator and wind farm operating states, and the data is processed by importing the numerical weather forecast data, the real-time anemometer tower data, the wind turbine generator and the wind farm into an overall power model for operation, performing integrity and rationality check, correcting missing measurement and abnormal data, and finally storing the data in a database.
The design is that data items of collected data are limited, numerical weather forecast data, real-time anemometer tower data, a wind turbine generator and a wind power plant are led into an overall power model to be operated in the data processing process, and the data are stored in a database after the stability of the data is ensured.
Referring to fig. 4, in S6, the wind power prediction stage includes:
s601: predicting the output power of the wind power plant according to the wind power of the wind power plant section;
s602: and predicting the wind power output power of a single wind power plant, a local control area and the whole scheduling jurisdiction area through the wind power of the power grid scheduling end.
The design is that steps of a wind power prediction stage are refined, so that the function of predicting wind power output power of a single wind power plant, a local control area and the whole scheduling jurisdiction area is realized.
Referring to fig. 1 and S7, the statistical analysis includes data statistics, correlation verification, error statistics on the prediction result of any time interval, error statistics on the prediction curves reported by the wind power plants in different scheduling jurisdictions, the optimization specifically includes comparing whether the wind power rate is optimal under the environmental factors, if so, checking and executing, if not, adjusting the thrust coefficient of the wind turbine, performing cyclic operation until the optimal value is reached, the data statistics specifically includes parameter statistics of historical power data, wind measurement data, integrity, frequency distribution, change rate and the like of numerical weather forecast data, the wind power plant operation parameter statistics includes power generation amount, effective power generation time, maximum output time and occurrence time thereof, coincidence rate, utilization hours and average coincidence rate, the correlation verification specifically includes performing verification on the historical power data, the wind measurement data and the numerical weather forecast data, errors possibly introduced by the data are given according to the analysis result, the errors possibly introduced by the data are performed, and the error indexes of the error statistics on the prediction result of any time interval include root mean square error, average absolute error, correlation verification coefficient, and the correlation statistics on the prediction curves in different scheduling jurisdictions include uploading the scores and checking the correlation scores.
The design limits the items of statistical analysis, and realizes the functions of data statistics, correlation verification, error statistics of prediction results in any time interval, error statistics of prediction curves reported by wind power plants in different scheduling jurisdictions and the like.
The working principle is as follows: firstly, introducing wind speed, wind shear and turbulence intensity as environmental factors and a thrust coefficient of a most upstream wind turbine, introducing the power characteristics of a predicted wind turbine as operation basic terms, wherein the power characteristics comprise the aerodynamic characteristics of wing sections in blades and the pitch angle of each blade as operation basic terms, after the most upstream wind turbine is started, operating and outputting power through the thrust coefficient, specifically, calculating the actual output power of the most upstream wind turbine through the wind turbine on the basis of the power characteristics of the wind turbine comprising the aerodynamic characteristics of the wing sections in the blades and the pitch angle of each blade as operation basic terms, calculating the basic thrust coefficient of the wind turbine through the starting rotating speed of the wind turbine as an introduced value, introducing the environmental factors, calculating the actual output power of the most upstream wind turbine, calculating the wind speed, the wind shear and the turbulence intensity of the downstream wind turbine position through a Jensen model, and calculating the hydrodynamic shear WLMs, the method comprises the steps of carrying out numerical solution on a Navier-Stokes equation for controlling the whole flow field in a wind power plant, assuming that the downstream of a trail is expanded through axial symmetry described by a constant trail attenuation constant (kW), calculating wind speed attenuation in a turbine trail, in the implementation operation, firstly collecting and processing data, wherein the data are processed, namely, numerical weather forecast data, real-time anemometer tower data, a wind turbine unit and the wind power plant are led into an overall power model for operation, integrity and reasonableness tests are carried out, missing and abnormal data are corrected, and finally the data are stored in a database, the output power of the wind power plant is predicted through the wind power of a wind power plant section in a wind power prediction stage, the wind power output power of a single wind power plant, a local control area and a whole scheduling jurisdiction area is predicted through the wind power of a power grid scheduling end, and finally statistical analysis and optimization are carried out, the statistical analysis comprises data statistics, correlation verification, error statistics on the prediction result of any time interval, error statistics on the prediction curve reported by each wind power plant in different scheduling jurisdictions, the optimization specifically comprises the steps of comparing whether the wind power rate is optimal under the environmental factors, if so, checking and executing, if not, adjusting the thrust coefficient of the wind turbine, performing cyclic operation until the optimal value is reached, the data statistics specifically comprises the parameters statistics such as historical power data, wind measurement data and the integrity, frequency distribution, change rate and the like of numerical weather forecast data, the wind power plant operation parameter statistics comprises the parameters such as generated energy, effective power generation time, maximum output and generation time, simultaneous rate, utilization hours and average coincidence rate, the correlation verification specifically comprises the steps of performing correlation verification on the historical power data, the wind measurement data and the numerical weather forecast data, errors possibly introduced by the data are given according to the analysis result, the error indexes for performing error statistics on the prediction result of any time interval comprise root mean square error, average absolute error and correlation coefficient, and the electric quantity of each prediction curve in different scheduling jurisdictions is subjected to electric quantity statistics, and electric quantity reporting, and fraction uploading statistics.
Although the present invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes and modifications may be made without departing from the spirit and scope of the invention.
Claims (10)
1. The wind power prediction and stock wind power optimization control method based on the wake effect is characterized by comprising the following steps of:
s1: firstly, introducing environmental factors and a thrust coefficient of a most upstream wind turbine, and introducing the predicted power characteristics of the wind turbine as an operation basic item;
s2: after the most upstream wind turbine is started, calculating output power through the thrust coefficient of the most upstream wind turbine;
s3: calculating the environmental factors of the position of the downstream wind turbine so as to calculate the output power of the downstream wind turbine;
s4: the operation result is changed into the output power of the upstream wind turbine, so that the environmental factors and the output power of the positions of the downstream wind turbines are calculated until the output power of the most downstream wind turbine is calculated, and the output power of the wind turbines at all positions is used as an extension operation item to derive an integral power model;
s5: in the implementation operation, firstly, data are collected and processed;
s6: a wind power prediction stage;
s7: and (4) performing statistical analysis and optimization.
2. The wake effect-based wind power prediction and stock wind power optimization control method of claim 1, characterized in that: in the step S1, the environmental factors include wind speed, wind shear and turbulence intensity, and the dynamic characteristics of the wind turbine include aerodynamic characteristics of an airfoil profile in the blade and a pitch angle of each blade.
3. The wake effect-based wind power prediction and stock wind power optimization control method of claim 1, characterized in that: in S2, the calculating of the output power by the thrust coefficient includes:
s201: the method comprises the steps that the aerodynamic characteristics of an airfoil profile in a blade and the pitch angle of each blade are used as basic operation terms based on the dynamic characteristics of a wind turbine;
s202: calculating a basic thrust coefficient of the wind turbine by taking the starting rotating speed of the wind turbine as an input value;
s203: and (4) calculating the actual output power of the uppermost stream wind turbine during starting according to the environmental factors.
4. The wake effect-based wind power prediction and stock wind power optimization control method of claim 1, characterized in that: in the step S3, the calculating of the environmental factors of the downstream wind turbine position is specifically to calculate the wind speed, the wind shear, and the turbulence intensity of the downstream wind turbine position through a Jensen model.
5. The wake effect-based wind power prediction and stock wind power optimization control method of claim 4, characterized in that: the operation of the wind speed, the wind shear and the turbulence intensity of the downstream wind turbine position through the Jensen model comprises the following steps:
s301: based on WLMs of computational fluid mechanics, numerical solution is carried out on a Navier-Stokes equation for controlling the whole flow field in the wind power plant;
s302: the wind speed decay in the turbine wake is calculated assuming axisymmetric expansion downstream of the wake described by a constant wake decay constant (kW).
6. The wake effect-based wind power prediction and stock wind power optimization control method according to claim 1, characterized in that: in S5, the collected data comprise numerical weather forecast data, real-time anemometer tower data, wind turbine generator set and wind power plant operation states.
7. The wake effect-based wind power prediction and stock wind power optimization control method of claim 1, characterized in that: and S5, processing the data specifically comprises the steps of importing numerical weather forecast data, real-time anemometer tower data, a wind turbine generator and a wind power plant into an overall power model for operation, carrying out integrity and reasonability check, correcting missing measurement and abnormal data, and finally storing the data into a database.
8. The wake effect-based wind power prediction and stock wind power optimization control method of claim 1, characterized in that: in S6, the wind power prediction stage includes:
s601: predicting the output power of the wind power plant through the wind power of the wind power plant section;
s602: and predicting the wind power output power of a single wind power plant, a local control area and the whole scheduling jurisdiction area through the wind power of the power grid scheduling end.
9. The wake effect-based wind power prediction and stock wind power optimization control method of claim 1, characterized in that: in the step S7, the statistical analysis comprises data statistics, correlation verification, error statistics on prediction results of any time interval and error statistics on prediction curves reported by each wind power plant in different scheduling administration ranges, the optimization specifically comprises the steps of comparing whether the wind power rate is optimal under the environmental factors, if so, checking and executing, if not, adjusting the thrust coefficient of the wind turbine, and performing circular operation until the optimal value is reached.
10. The wake effect-based wind power prediction and wind power inventory optimization control method according to claim 9, characterized in that: the data statistics specifically comprise historical power data, wind measurement data, integrity, frequency distribution, change rate and the like of numerical weather forecast data, the wind power plant operation parameter statistics comprise parameter statistics such as generated energy, effective power generation time, maximum output and generation time thereof, coincidence rate, utilization hours and average coincidence rate, the correlation verification specifically comprises correlation verification of the historical power data, the wind measurement data and the numerical weather forecast data, errors possibly introduced by uncertainty of the data are given according to analysis results, error indexes for performing error statistics on prediction results of any time interval comprise root mean square errors, average absolute errors and correlation coefficients, and the error statistics on prediction curves reported by the wind power plants in different scheduling administration ranges comprise assessment scores, electricity deduction amounts and uploading rates.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211142794.6A CN115544907A (en) | 2022-09-20 | 2022-09-20 | Wind power prediction and stock wind power optimization control method based on wake effect |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211142794.6A CN115544907A (en) | 2022-09-20 | 2022-09-20 | Wind power prediction and stock wind power optimization control method based on wake effect |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115544907A true CN115544907A (en) | 2022-12-30 |
Family
ID=84727973
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211142794.6A Pending CN115544907A (en) | 2022-09-20 | 2022-09-20 | Wind power prediction and stock wind power optimization control method based on wake effect |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115544907A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116191571A (en) * | 2023-04-17 | 2023-05-30 | 深圳量云能源网络科技有限公司 | Wind power energy storage output power control method and system |
-
2022
- 2022-09-20 CN CN202211142794.6A patent/CN115544907A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116191571A (en) * | 2023-04-17 | 2023-05-30 | 深圳量云能源网络科技有限公司 | Wind power energy storage output power control method and system |
| CN116191571B (en) * | 2023-04-17 | 2023-08-04 | 深圳量云能源网络科技有限公司 | Wind power energy storage output power control method and system |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Xudong et al. | Shape optimization of wind turbine blades | |
| US9201410B2 (en) | Methods and systems for optimizing farm-level metrics in a wind farm | |
| CN102914969B (en) | Comprehensive error correction method of short-period wind power prediction system | |
| CN114169614B (en) | Wind power plant optimal scheduling method and system based on wind turbine wake model optimization | |
| CN106058941B (en) | A kind of wind power plant random optimization dispatching method based on scene analysis | |
| CN111900743A (en) | Wind power frequency modulation potential prediction error distribution estimation method | |
| CN103296701B (en) | Active power control method in wind power plant | |
| CN103683326A (en) | Method for calculating optimal admitting ability for wind power multipoint access of regional power grid | |
| CN103244348A (en) | Power curve optimization method for variable-speed variable-pitch wind generating set | |
| CN112145373B (en) | Wind speed correction method for wind meter of wind driven generator | |
| CN105512766A (en) | Wind power plant power predication method | |
| CN115544907A (en) | Wind power prediction and stock wind power optimization control method based on wake effect | |
| CN116316883B (en) | Optimization control method for cooperative operation of wind turbine clusters | |
| CN113153633A (en) | Static deviation calibration method for wind direction instrument of wind turbine generator | |
| Liu et al. | Optimization model for rotor blades of horizontal axis wind turbines | |
| CN111445055A (en) | Method for optimizing model selection of wind generating set | |
| CN115906616A (en) | Method for calculating theoretical power and reactive power generation capacity of wind power plant | |
| CN116702957A (en) | New energy power prediction method, equipment and storage medium for extreme weather | |
| EP3741991B1 (en) | Method for dynamic real-time optimization of the performance of a wind park and wind park | |
| Tahani et al. | Aerodynamic optimal design of wind turbine blades using genetic algorithm | |
| CN113988421A (en) | Wind turbine generator power intelligent prediction method based on deep learning | |
| Shi et al. | Reliability assessment of interconnected generation systems based on hourly wind speed probability model | |
| Ashuri et al. | Uncertainty quantification of the levelized cost of energy for a 20 MW research wind turbine model | |
| CN111931967B (en) | Short-term power prediction method for wind power plant | |
| CN113919972A (en) | Small hydropower station daily load distribution method based on water turbine comprehensive characteristic curve |
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 |