[go: up one dir, main page]

CN119333332A - Wind turbine blade pitch adjustment method and device based on wind direction turbulence intensity - Google Patents

Wind turbine blade pitch adjustment method and device based on wind direction turbulence intensity Download PDF

Info

Publication number
CN119333332A
CN119333332A CN202411884845.1A CN202411884845A CN119333332A CN 119333332 A CN119333332 A CN 119333332A CN 202411884845 A CN202411884845 A CN 202411884845A CN 119333332 A CN119333332 A CN 119333332A
Authority
CN
China
Prior art keywords
wind
pitch angle
wind direction
turbulence intensity
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.)
Granted
Application number
CN202411884845.1A
Other languages
Chinese (zh)
Other versions
CN119333332B (en
Inventor
张立栋
冯正聪
许浩雨
曹善桥
张磊
张端梅
石强
陈怡冰
李佩
田史琳
杨智翔
杨世宇
韦福龙
刘阳
宋长鹏
李国浩
张肇南
徐一民
程群
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Datang Renewable Energy Test And Research Institute Co ltd
Northeast Electric Power University
Original Assignee
Datang Renewable Energy Test And Research Institute Co ltd
Northeast Dianli University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Datang Renewable Energy Test And Research Institute Co ltd, Northeast Dianli University filed Critical Datang Renewable Energy Test And Research Institute Co ltd
Priority to CN202411884845.1A priority Critical patent/CN119333332B/en
Publication of CN119333332A publication Critical patent/CN119333332A/en
Application granted granted Critical
Publication of CN119333332B publication Critical patent/CN119333332B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/022—Adjusting aerodynamic properties of the blades
    • F03D7/0224—Adjusting blade pitch
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F03D17/005—Monitoring or testing of wind motors, e.g. diagnostics using computation methods, e.g. neural networks
    • F03D17/006—Estimation methods
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
    • F03D17/007—Wind farm monitoring
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/028—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor controlling wind motor output power
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/043—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic
    • F03D7/045—Automatic control; Regulation by means of an electrical or electronic controller characterised by the type of control logic with model-based controls
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
    • F03D—WIND MOTORS
    • F03D7/00—Controlling wind motors 
    • F03D7/02—Controlling wind motors  the wind motors having rotation axis substantially parallel to the air flow entering the rotor
    • F03D7/04—Automatic control; Regulation
    • F03D7/042—Automatic control; Regulation by means of an electrical or electronic controller
    • F03D7/048—Automatic control; Regulation by means of an electrical or electronic controller controlling wind farms
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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]
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/32—Wind speeds
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/321—Wind directions
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/30—Control parameters, e.g. input parameters
    • F05B2270/328—Blade pitch angle
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
    • F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
    • F05B2270/00—Control
    • F05B2270/40—Type of control system
    • F05B2270/404—Type of control system active, predictive, or anticipative
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2113/00—Details relating to the application field
    • G06F2113/08—Fluids
    • 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)
  • General Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Sustainable Energy (AREA)
  • Mechanical Engineering (AREA)
  • Combustion & Propulsion (AREA)
  • Chemical & Material Sciences (AREA)
  • Sustainable Development (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Mathematical Optimization (AREA)
  • Pure & Applied Mathematics (AREA)
  • Mathematical Analysis (AREA)
  • Data Mining & Analysis (AREA)
  • Algebra (AREA)
  • Fluid Mechanics (AREA)
  • Evolutionary Computation (AREA)
  • Computational Mathematics (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • Computer Hardware Design (AREA)
  • Software Systems (AREA)
  • Geometry (AREA)
  • Probability & Statistics with Applications (AREA)
  • Operations Research (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Artificial Intelligence (AREA)
  • Wind Motors (AREA)

Abstract

本发明公开一种基于风向湍流强度的风电机组叶片变桨调节方法及装置，属于风力发电技术领域，该方法根据历史风速、历史风向进行风向湍流强度计算；利用最佳变桨角度计算模型根据风电机组的机组运行数据及风向湍流强度，计算当前风场条件下的最优输出功率的变桨角度；通过变桨角度预测模型根据风场条件变化，对指定时段的风电机组动态叶片变桨角度进行预测；根据预测的风电机组动态叶片变桨角度，向风电机组发送叶片变桨角度调整指令，并通过反馈机制对叶片变桨角度的预测结果进行验证和修正，以达到风电机组的功率输出最优化。本发明考虑风向湍流对风机的动态负载影响，自适应性强，实现不同风况下的最优控制，可以灵活应对复杂风况变化。

The present invention discloses a method and device for adjusting the blade pitch of a wind turbine set based on the wind direction turbulence intensity, belonging to the technical field of wind power generation. The method calculates the wind direction turbulence intensity according to the historical wind speed and the historical wind direction; uses the optimal pitch angle calculation model to calculate the pitch angle of the optimal output power under the current wind field conditions according to the unit operation data of the wind turbine set and the wind direction turbulence intensity; predicts the dynamic blade pitch angle of the wind turbine set in a specified period according to the change of wind field conditions through the pitch angle prediction model; sends a blade pitch angle adjustment instruction to the wind turbine set according to the predicted dynamic blade pitch angle of the wind turbine set, and verifies and corrects the prediction result of the blade pitch angle through a feedback mechanism, so as to achieve the optimization of the power output of the wind turbine set. The present invention takes into account the influence of wind direction turbulence on the dynamic load of the wind turbine, has strong adaptability, realizes the optimal control under different wind conditions, and can flexibly cope with complex wind condition changes.

Description

Wind turbine generator blade pitch-variable adjusting method and device based on wind direction turbulence intensity
Technical Field
The invention belongs to the technical field of wind power generation, and particularly relates to a wind turbine generator blade pitch adjustment method and device based on wind direction turbulence intensity.
Background
At present, in the wind power generation process, the pitch angle adjustment of the blades of the wind turbine generator is one of key factors influencing the power generation efficiency and the service life of equipment.
At present, wind turbine control systems typically focus on wind speed and average wind direction, and lack consideration on dynamic fluctuation of wind direction, i.e., wind direction turbulence intensity. Wind direction turbulence is particularly remarkable when the wind speed is high, and the turbulence effect directly influences the aerodynamic characteristics of the blade, so that the stress of the blade changes sharply, and the fatigue loss of the blade and the main shaft is increased. The fluctuation caused by wind direction turbulence intensity is not fully considered in the prior art, so that the variable pitch angle of the blade cannot adapt to complex wind direction change, the output power is unstable, and the equipment wear is aggravated.
In addition, current pitch systems of wind turbines typically rely on simple real-time wind speed and wind direction data to adjust blade angle, making it difficult to effectively predict dramatic changes in wind conditions. The traditional control system has lag reaction when dealing with sudden turbulence, particularly under the wind condition of high turbulence intensity, the rapid change of the wind direction cannot be predicted in advance, so that the control system cannot adjust the pitch angle in time, and the power output is difficult to reach the optimal. The pitch angle adjustment in the prior art is often based on preset parameters, lacks self-adaptability, cannot be intelligently adjusted according to actual wind conditions, and is difficult to realize optimal control under different wind conditions. Such non-intelligent control modes result in excessive equipment load or low power output efficiency and are not flexible to cope with complex wind condition changes.
Disclosure of Invention
Therefore, the invention provides a wind turbine generator blade pitch adjustment method and device based on wind direction turbulence intensity, which solve the problems that the prior art fails to consider the influence of wind direction turbulence on the dynamic load of a fan, lacks self-adaptability, cannot intelligently adjust according to actual wind conditions, is difficult to realize optimal control under different wind conditions, and cannot flexibly cope with complex wind condition changes.
In order to achieve the purpose, the invention provides the following technical scheme that the wind turbine generator blade pitch adjustment method based on wind direction turbulence intensity comprises the following steps:
s1, acquiring appointed historical data of a wind field where a wind turbine generator is located, wherein the appointed historical data comprises a historical wind speed and a historical wind direction;
S2, constructing an optimal pitch angle calculation model, and calculating the pitch angle of the optimal output power under the current wind field condition according to the unit operation data of the wind turbine and the wind direction turbulence intensity by the optimal pitch angle calculation model;
S3, constructing a variable pitch angle prediction model, and predicting the variable pitch angle of the dynamic blades of the wind turbine in a specified period according to the change of wind field conditions through the variable pitch angle prediction model so as to respond to the impending turbulence fluctuation, so that the variable pitch angle of the wind turbine is adjusted to be optimal in real time;
S4, sending a blade pitch angle adjustment instruction to the wind turbine generator according to the predicted wind turbine generator dynamic blade pitch angle, and verifying and correcting a predicted result of the blade pitch angle through a feedback mechanism so as to achieve power output optimization of the wind turbine generator.
As a preferable scheme of the wind turbine generator blade pitch adjustment method based on wind direction turbulence intensity, a formula for calculating wind direction turbulence intensity according to the historical wind speed and the historical wind direction is as follows:
;
In the formula, The wind direction turbulence intensity is represented by quantification of wind direction fluctuation; Representing the number of samples; Represent the first The wind direction variation of each time step; Represent the first The wind direction variation of each time step; Represent the first The wind direction variation of each time step; representing the instantaneous wind speed; Representing the average wind speed in the historical data.
As a preferable scheme of the wind turbine generator blade pitch adjustment method based on wind direction turbulence intensity, the expression formula of the optimal pitch angle calculation model is as follows:
;
In the formula, Representing an optimal pitch angle; Is the wind direction turbulence intensity influence coefficient; is a power influence coefficient; Historical generating capacity data; Representing the offset.
As a preferable scheme of the wind turbine generator blade pitch adjustment method based on wind direction turbulence intensity, the expression formula of the pitch angle prediction model is as follows:
;
In the formula, Representing futurePredicting a possible pitch angle at moment; a wind direction turbulence change rate coefficient is expressed and used for enhancing the sensitivity of the pitch response; Representing the instantaneous turbulence intensity variation; The average value of the turbulence intensity is shown.
As a preferable scheme of the wind turbine generator blade pitch adjustment method based on wind direction turbulence intensity, the method further comprises the step of training the characteristics of the pitch angle prediction model by combining a multi-layer neural network model, wherein the structure of the pitch angle prediction model after training is as follows:
;
In the formula, Is responsible for inputting the characteristics of the character,The method comprises the steps of forming a characteristic vector set by wind direction turbulence intensity, wind speed change rate and generating capacity fluctuation amplitude in historical data; Performing nonlinear transformation by adopting an activation function (ReLU); Is the optimal pitch angle for final output.
As a preferable scheme of the wind turbine generator blade pitch adjustment method based on wind direction turbulence intensity, verifying and correcting the prediction result of the blade pitch angle through a feedback mechanism comprises the following steps:
The output power of the wind turbine generator is monitored in real time, compared with a theoretical optimal value, and if the deviation exceeds a set threshold value, the coefficient of the optimal pitch angle calculation model is calculated 、、Correcting parameters;
If the fluctuation of the wind direction turbulence intensity exceeds a threshold value, the sensitivity index wind direction turbulence change rate coefficient of the pitch adjustment is increased 。
The invention also provides a wind turbine generator blade pitch adjusting device based on wind direction turbulence intensity, which comprises:
the wind direction turbulence intensity analysis module is used for acquiring appointed historical data of a wind field where the wind turbine generator is located, wherein the appointed historical data comprises a historical wind speed and a historical wind direction;
The current pitch angle analysis module is used for constructing an optimal pitch angle calculation model, and the optimal pitch angle calculation model calculates the pitch angle of the optimal output power under the current wind field condition according to the unit operation data of the wind turbine and the wind direction turbulence intensity;
the variable pitch angle dynamic prediction module is used for constructing a variable pitch angle prediction model, predicting the variable pitch angle of the dynamic blades of the wind turbine in a specified period according to the change of wind field conditions through the variable pitch angle prediction model so as to respond to the impending turbulence fluctuation, and enabling the variable pitch angle of the wind turbine to be adjusted to be optimal in real time;
The pitch angle command adjusting module is used for sending a blade pitch angle adjusting command to the wind turbine generator according to the predicted pitch angle of the dynamic blade of the wind turbine generator, and verifying and correcting the predicted result of the blade pitch angle through a feedback mechanism so as to achieve the power output optimization of the wind turbine generator.
As a preferable scheme of the wind turbine generator blade pitch adjusting device based on wind direction turbulence intensity, in the wind direction turbulence intensity analysis module, a formula for calculating wind direction turbulence intensity according to the historical wind speed and the historical wind direction is as follows:
;
In the formula, The wind direction turbulence intensity is represented by quantification of wind direction fluctuation; Representing the number of samples; Represent the first The wind direction variation of each time step; Represent the first The wind direction variation of each time step; Represent the first The wind direction variation of each time step; representing the instantaneous wind speed; Representing the average wind speed in the historical data.
As a preferable scheme of the wind turbine generator blade pitch adjusting device based on wind direction turbulence intensity, in the current pitch angle analysis module, the expression formula of the optimal pitch angle calculation model is as follows:
;
In the formula, Representing an optimal pitch angle; Is the wind direction turbulence intensity influence coefficient; is a power influence coefficient; Historical generating capacity data; Representing the offset.
As a preferable scheme of the wind turbine generator blade pitch adjusting device based on wind direction turbulence intensity, in the pitch angle dynamic prediction module, an expression formula of the pitch angle prediction model is as follows:
;
In the formula, Representing futurePredicting a possible pitch angle at moment; a wind direction turbulence change rate coefficient is expressed and used for enhancing the sensitivity of the pitch response; Representing the instantaneous turbulence intensity variation; Mean value of turbulence intensity;
The variable pitch angle dynamic prediction module is further used for training the characteristics of the variable pitch angle prediction model by combining a multi-layer neural network model, and the structure of the variable pitch angle prediction model after training is as follows:
;
In the formula, Is responsible for inputting the characteristics of the character,The method comprises the steps of forming a characteristic vector set by wind direction turbulence intensity, wind speed change rate and generating capacity fluctuation amplitude in historical data; Performing nonlinear transformation by adopting an activation function (ReLU); Is the optimal pitch angle for final output.
As a preferable scheme of the wind turbine generator system blade pitch adjustment device based on wind direction turbulence intensity, in the pitch angle command adjustment module, verifying and correcting the prediction result of the blade pitch angle through a feedback mechanism comprises:
The output power of the wind turbine generator is monitored in real time, compared with a theoretical optimal value, and if the deviation exceeds a set threshold value, the coefficient of the optimal pitch angle calculation model is calculated 、、Correcting parameters;
If the fluctuation of the wind direction turbulence intensity exceeds a threshold value, the sensitivity index wind direction turbulence change rate coefficient of the pitch adjustment is increased 。
The invention has the following advantages:
The invention introduces wind direction turbulence intensity as a control parameter for adjusting the pitch angle so as to quantify the influence of the fluctuation of wind direction on the pitch angle, can identify the impact of turbulence on blades by combining with the operation data of a wind turbine generator by calculating the wind direction turbulence intensity, dynamically adjusts the pitch angle so as to adapt to different turbulence conditions, overcomes the problem that the prior art can not consider the influence of wind direction turbulence on the dynamic load of a fan, and ensures that the fan maintains stable output power under high turbulence intensity;
the invention can predict the optimal pitch angle under the future wind condition according to the wind turbulence rule in the historical data and adjust in advance, overcomes the defects that the prior art only depends on real-time data and is difficult to respond to the wind condition change in real time, ensures that the pitch control is more intelligent and prospective, and greatly improves the power output stability of the wind turbine generator;
The invention can correct the predicted value of the pitch angle through a feedback mechanism on the basis of collecting the wind speed and wind direction data in real time, so that the pitch angle is more in line with the current wind condition requirement, the self-adaption and control precision of the system are obviously improved, the equipment loss is reduced, the power output efficiency is improved, and the problems that the optimal control under different wind conditions is difficult to realize and the complex wind condition change cannot be flexibly dealt with in the prior art are solved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.
FIG. 1 is a schematic flow chart of a method for adjusting the pitch of a wind turbine blade based on wind direction turbulence intensity provided in an embodiment of the invention;
FIG. 2 is a predicted value of a pitch angle in a simulation verification process of a pitch adjustment method of a wind turbine generator blade based on wind direction turbulence intensity provided in an embodiment of the invention;
FIG. 3 is a comparison of blade pitch angles before and after optimization in a simulation verification process of a wind turbine blade pitch adjustment method based on wind direction turbulence intensity provided in an embodiment of the present invention;
fig. 4 is a schematic diagram of a wind turbine blade pitch adjustment device based on wind direction turbulence intensity according to an embodiment of the present invention.
Detailed Description
Other advantages and advantages of the present invention will become apparent to those skilled in the art from the following detailed description, which, by way of illustration, is to be read in connection with certain specific embodiments, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, an embodiment of the invention provides a wind turbine generator blade pitch adjustment method based on wind direction turbulence intensity, which comprises the following steps:
s1, acquiring appointed historical data of a wind field where a wind turbine generator is located, wherein the appointed historical data comprises a historical wind speed and a historical wind direction;
S2, constructing an optimal pitch angle calculation model, and calculating the pitch angle of the optimal output power under the current wind field condition according to the unit operation data of the wind turbine and the wind direction turbulence intensity by the optimal pitch angle calculation model;
S3, constructing a variable pitch angle prediction model, and predicting the variable pitch angle of the dynamic blades of the wind turbine in a specified period according to the change of wind field conditions through the variable pitch angle prediction model so as to respond to the impending turbulence fluctuation, so that the variable pitch angle of the wind turbine is adjusted to be optimal in real time;
S4, sending a blade pitch angle adjustment instruction to the wind turbine generator according to the predicted wind turbine generator dynamic blade pitch angle, and verifying and correcting a predicted result of the blade pitch angle through a feedback mechanism so as to achieve power output optimization of the wind turbine generator.
In this embodiment, in step S1, first, historical data of a wind field where a wind turbine generator is located is analyzed, and by processing sampled data of a historical wind speed and a historical wind direction, a fluctuation condition of the wind direction is extracted, and the fluctuation condition of the wind direction is represented by an index of wind direction turbulence intensity. The formula for wind direction turbulence intensity calculation according to the historical wind speed and the historical wind direction is as follows:
;
In the formula, The wind direction turbulence intensity is represented by quantification of wind direction fluctuation; Representing the number of samples; Represent the first The wind direction variation of each time step; Represent the first The wind direction variation of each time step; Represent the first The wind direction variation of each time step; representing the instantaneous wind speed; Representing the average wind speed in the historical data.
In particular, wind direction turbulence intensityThe influence of the change rate of the wind direction and the wind speed is expressed. The formula is used for quantifying the turbulence intensity of wind direction, amplifying the turbulence influence through a quadratic term of wind direction change, and capturing the influence of the turbulence intensity of wind direction under different wind speeds by combining the weighted proportion of instantaneous wind speeds, thereby providing a basis for the subsequent pitch angle calculation.
In the embodiment, in step S2, an optimal pitch angle calculation model is constructed by comprehensively analyzing the historical power generation amount, pitch angle and wind speed of the unit, so as to adapt to the conditions of turbulence intensity of different wind directions. Specifically, the expression formula of the optimal pitch angle calculation model is as follows:
;
In the formula, Representing an optimal pitch angle; the wind direction turbulence intensity influence coefficient reflects the direct influence degree of wind direction turbulence intensity on the pitch angle; The correction effect of the generated energy on the pitch angle is reflected as the power influence coefficient; the historical generating capacity data are used for reflecting the influence of wind direction turbulence intensity on power output; and representing the offset, and adjusting the pitch angle through environmental adaptation.
The optimal pitch angle calculation model calculates a pitch angle capable of optimally outputting power under the current wind field condition by weighting wind direction turbulence intensity and power output. By adjusting、、And the coefficients are equal, so that the environmental characteristics and the unit power requirements of different wind fields can be adapted. Wind direction turbulence intensity weightProviding feedback of turbulence to blade adjustment. Generating capacity weightAnd the optimal combination of wind speed, turbulence intensity and power output is realized according to the optimal pitch angle at the balance position of the relation between wind speed and power generation.
In this embodiment, in step S3, in order to improve the real-time response capability of pitch adjustment, a possible pitch angle is predicted by combining the wind direction and the wind speed in a future period of time, where an expression formula of the pitch angle prediction model is:
;
In the formula, Representing futurePredicting a possible pitch angle at moment; a wind direction turbulence change rate coefficient is expressed and used for enhancing the sensitivity of the pitch response; Representing the instantaneous turbulence intensity variation; The average value of the turbulence intensity is shown.
The method further comprises the step of training the characteristics of the pitch angle prediction model by combining a multi-layer neural network model, wherein the structure of the pitch angle prediction model after training is as follows:
;
In the formula, Is responsible for inputting the characteristics of the character,The method comprises the steps of forming a characteristic vector set by wind direction turbulence intensity, wind speed change rate and generating capacity fluctuation amplitude in historical data; Performing nonlinear transformation by adopting an activation function (ReLU); Is the optimal pitch angle for final output.
Specifically, a formula of the variable pitch angle prediction model provides dynamic variable pitch angle prediction according to wind field condition changes, and the model is trained by combining a multi-layer neural network to optimize the predicted variable pitch angle of the blade. By monitoring the change of the wind direction turbulence intensity and feeding the change back to the real-time control, the wind turbine generator system can respond to the impending turbulence fluctuation in advance, so that the pitch angle of the wind turbine generator system is adjusted to be optimal in real time. Turbulence variation ratio term inReflecting the burstiness of turbulence and its amplitude, regulating factorThe system can quickly respond to wind condition fluctuation, and the stability of output power is optimized.
In this embodiment, in step S4, based on the predicted pitch angle, the control system sends an adjustment instruction to the pitch controller of the wind turbine, and verifies and corrects the predicted result by using a feedback mechanism, so as to form a closed-loop control system, so as to achieve optimization of power output.
Specifically, verifying and correcting the prediction result of the variable pitch angle of the blade through a feedback mechanism includes:
The output power of the wind turbine generator is monitored in real time, compared with a theoretical optimal value, and if the deviation exceeds a set threshold value, the coefficient of the optimal pitch angle calculation model is calculated 、、Correcting parameters;
If the fluctuation of the wind direction turbulence intensity exceeds a threshold value, the sensitivity index wind direction turbulence change rate coefficient of the pitch adjustment is increased 。
Referring to fig. 2, 1 hour of history data is selected as a data set for training and prediction. And training the characteristics by combining a multi-layer neural network model and predicting the pitch angle in a future period of time. Meanwhile, analysis is performed based on wind direction turbulence emphasis, historical pitching data and predicted pitching data are comprehensively analyzed, the historical pitching data and the predicted pitching data are used as reference to optimize pitching control parameters, the pitching position of the blade after the pitching control parameters are optimized is compared with an original strategy, and the result is shown in fig. 3. It can be seen that the optimized variable pitch angle is relatively stable, the variation amplitude is small, the abrasion of unit equipment can be effectively reduced, and the variable pitch power consumption is reduced.
In summary, the embodiment of the invention obtains appointed historical data of a wind field where a wind turbine is located, wherein the appointed historical data comprises historical wind speed and historical wind direction, wind direction turbulence intensity calculation is carried out according to the historical wind speed and the historical wind direction, an optimal pitch angle calculation model is built, the optimal pitch angle calculation model calculates the pitch angle of optimal output power under the current wind field condition according to the turbine operation data of the wind turbine and the wind direction turbulence intensity, a pitch angle prediction model is built, the pitch angle of dynamic blades of the wind turbine in an appointed period is predicted according to the wind field condition change through the pitch angle prediction model, the impending turbulence fluctuation is responded, the pitch angle of the wind turbine is adjusted to be optimal in real time, a blade pitch angle adjustment instruction is sent to the wind turbine according to the predicted dynamic blade pitch angle of the wind turbine, and the predicted result of the blade pitch angle is verified and corrected through a feedback mechanism so as to achieve power output optimization of the wind turbine. The wind direction turbulence intensity is used as a control parameter for adjusting the pitch angle, the influence of the fluctuation of wind direction on the pitch angle is quantized, the impact of turbulence on blades can be identified by calculating the wind direction turbulence intensity and combining with the operation data of a wind turbine generator, the pitch angle is dynamically adjusted to adapt to different turbulence conditions, the problem that the influence of wind direction turbulence on the dynamic load of a fan cannot be considered in the prior art is overcome, the fan keeps stable output power under high turbulence intensity is solved, the optimal pitch angle under future wind conditions can be predicted according to the rule of wind direction turbulence in historical data and adjusted in advance, the defects that the prior art only depends on real-time data and is difficult to respond to the change of wind conditions in real time are overcome, the pitch control is more intelligent and prospective, the power output stability of the wind turbine generator is greatly improved, the predicted value of the pitch angle is corrected through a feedback mechanism on the basis of collecting wind speed and wind direction data in real time, the self-adaption and control precision of the system are remarkably improved, the equipment loss is reduced, the optimal wind condition is improved, and the problem that the prior art is difficult to realize the optimal wind condition is difficult to control under different and flexible conditions is solved.
Example 2
Referring to fig. 4, embodiment 2 of the present invention further provides a wind turbine blade pitch adjustment device based on wind direction turbulence intensity, including:
The wind direction turbulence intensity analysis module 100 is used for acquiring appointed historical data of a wind field where the wind turbine generator is located, wherein the appointed historical data comprises a historical wind speed and a historical wind direction;
The current pitch angle analysis module 200 is used for constructing an optimal pitch angle calculation model, and the optimal pitch angle calculation model calculates the pitch angle of the optimal output power under the current wind field condition according to the set operation data of the wind turbine and the wind direction turbulence intensity;
The pitch angle dynamic prediction module 300 is configured to construct a pitch angle prediction model, predict a pitch angle of a dynamic blade of the wind turbine in a specified period according to a wind field condition change through the pitch angle prediction model, and respond to impending turbulence fluctuation so as to adjust the pitch angle of the wind turbine to be optimal in real time;
The pitch angle command adjustment module 400 is configured to send a pitch angle command to the wind turbine generator according to the predicted pitch angle of the dynamic blade of the wind turbine generator, and verify and correct the predicted result of the pitch angle of the blade through a feedback mechanism, so as to achieve power output optimization of the wind turbine generator.
In this embodiment, in the wind direction turbulence intensity analysis module 100, a formula for calculating the wind direction turbulence intensity according to the historical wind speed and the historical wind direction is:
;
In the formula, The wind direction turbulence intensity is represented by quantification of wind direction fluctuation; Representing the number of samples; Represent the first The wind direction variation of each time step; Represent the first The wind direction variation of each time step; Represent the first The wind direction variation of each time step; representing the instantaneous wind speed; Representing the average wind speed in the historical data.
In this embodiment, in the current pitch angle analysis module 200, the expression formula of the optimal pitch angle calculation model is:
;
In the formula, Representing an optimal pitch angle; Is the wind direction turbulence intensity influence coefficient; is a power influence coefficient; Historical generating capacity data; Representing the offset.
In this embodiment, the pitch angle dynamic prediction module 300, the expression formula of the pitch angle prediction model is:
;
In the formula, Representing futurePredicting a possible pitch angle at moment; a wind direction turbulence change rate coefficient is expressed and used for enhancing the sensitivity of the pitch response; Representing the instantaneous turbulence intensity variation; Mean value of turbulence intensity;
The pitch angle dynamic prediction module 300 is further configured to train the features of the pitch angle prediction model in combination with a multi-layer neural network model, where the structure of the pitch angle prediction model after training is as follows:
;
In the formula, Is responsible for inputting the characteristics of the character,The method comprises the steps of forming a characteristic vector set by wind direction turbulence intensity, wind speed change rate and generating capacity fluctuation amplitude in historical data; Performing nonlinear transformation by adopting an activation function (ReLU); Is the optimal pitch angle for final output.
In this embodiment, in the pitch angle command adjustment module 400, verifying and correcting the prediction result of the pitch angle of the blade through the feedback mechanism includes:
The output power of the wind turbine generator is monitored in real time, compared with a theoretical optimal value, and if the deviation exceeds a set threshold value, the coefficient of the optimal pitch angle calculation model is calculated 、、Correcting parameters;
If the fluctuation of the wind direction turbulence intensity exceeds a threshold value, the sensitivity index wind direction turbulence change rate coefficient of the pitch adjustment is increased 。
It should be noted that, because the content of information interaction and execution process between the modules of the above-mentioned device is based on the same concept as the method embodiment in the embodiment 1 of the present application, the technical effects brought by the content are the same as the method embodiment of the present application, and the specific content can be referred to the description in the foregoing illustrated method embodiment of the present application, which is not repeated herein.
Example 3
Embodiment 3 of the present invention provides a non-transitory computer readable storage medium having stored therein program code for a wind turbine blade pitch adjustment method based on wind direction turbulence intensity, the program code comprising instructions for performing the wind turbine blade pitch adjustment method based on wind direction turbulence intensity of embodiment 1 or any possible implementation thereof.
Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc., that contain an integration of one or more available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid state disk Solid STATE DISK, SSD), etc.
Example 4
The embodiment 4 of the invention provides electronic equipment, which comprises a memory and a processor;
the processor and the memory complete communication with each other through a bus, the memory stores program instructions executable by the processor, and the processor calls the program instructions to execute the wind turbine blade pitch adjustment method based on wind direction turbulence intensity of embodiment 1 or any possible implementation manner thereof.
The processor may be implemented by hardware or software, and when implemented by hardware, the processor may be a logic circuit, an integrated circuit, or the like, and when implemented by software, the processor may be a general-purpose processor, and by reading software codes stored in a memory, which may be integrated in the processor, may be located outside the processor, and exist independently.
In the above embodiments, it may be implemented in whole or in part by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, produces a flow or function in accordance with embodiments of the present invention, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by wired (e.g., coaxial cable, optical fiber, digital Subscriber Line (DSL)), or wireless (e.g., infrared, wireless, microwave, etc.).
It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may alternatively be implemented in program code executable by computing devices, so that they may be stored in a memory device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.
While the invention has been described in detail in the foregoing general description and specific examples, it will be apparent to those skilled in the art that modifications and improvements can be made thereto. Accordingly, such modifications or improvements may be made without departing from the spirit of the invention and are intended to be within the scope of the invention as claimed.

Claims (10)

1.基于风向湍流强度的风电机组叶片变桨调节方法，其特征在于，包括：1. A method for adjusting the pitch of wind turbine blades based on wind direction turbulence intensity, characterized by comprising: S1、获取风电机组所处风场的指定历史数据，所述指定历史数据包括历史风速、历史风向；根据所述历史风速、所述历史风向进行风向湍流强度计算；S1. Obtain designated historical data of the wind farm where the wind turbine is located, wherein the designated historical data includes historical wind speed and historical wind direction; and calculate the wind direction turbulence intensity according to the historical wind speed and the historical wind direction; S2、构建最佳变桨角度计算模型，所述最佳变桨角度计算模型根据所述风电机组的机组运行数据及所述风向湍流强度，计算当前风场条件下的最优输出功率的变桨角度；S2. constructing an optimal pitch angle calculation model, wherein the optimal pitch angle calculation model calculates the pitch angle of the optimal output power under the current wind field conditions according to the unit operation data of the wind turbine unit and the wind direction turbulence intensity; S3、构建变桨角度预测模型，通过所述变桨角度预测模型根据风场条件变化，对指定时段的风电机组动态叶片变桨角度进行预测，以对即将发生的湍流波动进行响应，使风电机组的变桨角度实时调整至最优；S3, constructing a pitch angle prediction model, and using the pitch angle prediction model to predict the pitch angle of the dynamic blades of the wind turbine in a specified period according to changes in wind field conditions, so as to respond to the impending turbulent fluctuations and adjust the pitch angle of the wind turbine to the optimal value in real time; S4、根据预测的风电机组动态叶片变桨角度，向风电机组发送叶片变桨角度调整指令，并通过反馈机制对叶片变桨角度的预测结果进行验证和修正，以达到所述风电机组的功率输出最优化。S4. Send a blade pitch angle adjustment instruction to the wind turbine according to the predicted dynamic blade pitch angle of the wind turbine, and verify and correct the predicted result of the blade pitch angle through a feedback mechanism to optimize the power output of the wind turbine. 2.根据权利要求1所述的基于风向湍流强度的风电机组叶片变桨调节方法，其特征在于，根据所述历史风速、所述历史风向进行风向湍流强度计算的公式为：2. The method for adjusting the wind turbine blade pitch based on wind direction turbulence intensity according to claim 1 is characterized in that the formula for calculating the wind direction turbulence intensity according to the historical wind speed and the historical wind direction is: ； ; 式中，表示风向湍流强度，是对风向波动的量化表示；表示样本数量；表示第个时间步长的风向变化量；表示第个时间步长的风向变化量；表示第个时间步长的风向变化量；表示瞬时风速；表示历史数据中的平均风速。In the formula, It indicates the wind turbulence intensity, which is a quantitative representation of wind direction fluctuations; represents the number of samples; Indicates The change in wind direction per time step; Indicates The change in wind direction per time step; Indicates The change in wind direction per time step; Indicates instantaneous wind speed; Represents the average wind speed in historical data. 3.根据权利要求2所述的基于风向湍流强度的风电机组叶片变桨调节方法，其特征在于，所述最佳变桨角度计算模型的表达公式为：3. The method for adjusting the wind turbine blade pitch based on wind direction turbulence intensity according to claim 2 is characterized in that the expression formula of the optimal pitch angle calculation model is: ； ; 式中，表示最佳变桨角度；为风向湍流强度影响系数；为功率影响系数；为历史发电量数据；表示偏移量。In the formula, Indicates the optimal pitch angle; is the influence coefficient of wind direction turbulence intensity; is the power influence coefficient; is the historical power generation data; Indicates the offset. 4.根据权利要求3所述的基于风向湍流强度的风电机组叶片变桨调节方法，其特征在于，所述变桨角度预测模型的表达公式为：4. The method for adjusting the pitch of wind turbine blades based on wind direction turbulence intensity according to claim 3 is characterized in that the expression formula of the pitch angle prediction model is: ； ; 式中，表示未来时刻可能的预测变桨角度；表示风向湍流变化率系数，用于增强变桨响应的灵敏度；表示瞬时湍流强度变化量；表示湍流强度的平均值。In the formula, Indicates the future The predicted pitch angle possible at any moment; Represents the wind direction turbulence change rate coefficient, which is used to enhance the sensitivity of pitch response; Indicates the instantaneous change in turbulence intensity; Represents the average value of turbulence intensity. 5.根据权利要求4所述的基于风向湍流强度的风电机组叶片变桨调节方法，其特征在于，还包括结合多层神经网络模型对所述变桨角度预测模型的特征进行训练，训练后的所述变桨角度预测模型的结构为：5. The method for adjusting the pitch of wind turbine blades based on wind direction turbulence intensity according to claim 4 is characterized in that it also includes training the characteristics of the pitch angle prediction model in combination with a multi-layer neural network model, and the structure of the trained pitch angle prediction model is: ； ; 式中，负责输入特征，包括历史数据中的风向湍流强度、风速变化率、发电量波动幅度，形成特征向量集；采用激活函数（ReLU）进行非线性变换；为最终输出的最佳变桨角度。In the formula, Responsible for inputting features, Including wind direction turbulence intensity, wind speed change rate, and power generation fluctuation amplitude in historical data to form a feature vector set; Use activation function (ReLU) for nonlinear transformation; is the optimal pitch angle for the final output. 6.根据权利要求5所述的基于风向湍流强度的风电机组叶片变桨调节方法，其特征在于，通过反馈机制对叶片变桨角度的预测结果进行验证和修正包括：6. The method for adjusting blade pitch of a wind turbine based on wind direction turbulence intensity according to claim 5 is characterized in that verifying and correcting the prediction result of blade pitch angle by feedback mechanism comprises: 实时监测所述风电机组的输出功率，与理论最优值对比，若偏差超过设定阈值，则对所述最佳变桨角度计算模型的系数、、参数进行修正；The output power of the wind turbine is monitored in real time and compared with the theoretical optimal value. If the deviation exceeds the set threshold, the coefficient of the optimal pitch angle calculation model is adjusted. , , Parameters are modified; 若风向湍流强度波动超过阈值，则增加变桨调节的灵敏度指标风向湍流变化率系数。If the wind direction turbulence intensity fluctuation exceeds the threshold, the wind direction turbulence change rate coefficient, which is the sensitivity index of pitch adjustment, is increased. . 7.基于风向湍流强度的风电机组叶片变桨调节装置，其特征在于，包括：7. A wind turbine blade pitch adjustment device based on wind direction turbulence intensity, characterized by comprising: 风向湍流强度分析模块，用于获取风电机组所处风场的指定历史数据，所述指定历史数据包括历史风速、历史风向；根据所述历史风速、所述历史风向进行风向湍流强度计算；A wind direction turbulence intensity analysis module is used to obtain designated historical data of the wind farm where the wind turbine is located, wherein the designated historical data includes historical wind speed and historical wind direction; and calculate the wind direction turbulence intensity according to the historical wind speed and the historical wind direction; 当前变桨角度分析模块，用于构建最佳变桨角度计算模型，所述最佳变桨角度计算模型根据所述风电机组的机组运行数据及所述风向湍流强度，计算当前风场条件下的最优输出功率的变桨角度；A current pitch angle analysis module is used to construct an optimal pitch angle calculation model, wherein the optimal pitch angle calculation model calculates the pitch angle of the optimal output power under the current wind field conditions according to the unit operation data of the wind turbine unit and the wind direction turbulence intensity; 变桨角度动态预测模块，用于构建变桨角度预测模型，通过所述变桨角度预测模型根据风场条件变化，对指定时段的风电机组动态叶片变桨角度进行预测，以对即将发生的湍流波动进行响应，使风电机组的变桨角度实时调整至最优；The pitch angle dynamic prediction module is used to construct a pitch angle prediction model, through which the pitch angle prediction model is used to predict the dynamic blade pitch angle of the wind turbine in a specified period according to the change of wind field conditions, so as to respond to the impending turbulent fluctuations and adjust the pitch angle of the wind turbine to the optimal value in real time; 变桨角度指令调整模块，用于根据预测的风电机组动态叶片变桨角度，向风电机组发送叶片变桨角度调整指令，并通过反馈机制对叶片变桨角度的预测结果进行验证和修正，以达到所述风电机组的功率输出最优化。The pitch angle instruction adjustment module is used to send a blade pitch angle adjustment instruction to the wind turbine according to the predicted dynamic blade pitch angle of the wind turbine, and verify and correct the predicted result of the blade pitch angle through a feedback mechanism to achieve the optimization of the power output of the wind turbine. 8.根据权利要求7所述的基于风向湍流强度的风电机组叶片变桨调节装置，其特征在于，所述风向湍流强度分析模块中，根据所述历史风速、所述历史风向进行风向湍流强度计算的公式为：8. The wind turbine blade pitch adjustment device based on wind direction turbulence intensity according to claim 7, characterized in that, in the wind direction turbulence intensity analysis module, the formula for calculating the wind direction turbulence intensity according to the historical wind speed and the historical wind direction is: ； ; 式中，表示风向湍流强度，是对风向波动的量化表示；表示样本数量；表示第个时间步长的风向变化量；表示第个时间步长的风向变化量；表示第个时间步长的风向变化量；表示瞬时风速；表示历史数据中的平均风速。In the formula, It indicates the wind turbulence intensity, which is a quantitative representation of wind direction fluctuations; represents the number of samples; Indicates The change in wind direction per time step; Indicates The change in wind direction per time step; Indicates The change in wind direction per time step; Indicates instantaneous wind speed; Represents the average wind speed in historical data. 9.根据权利要求8所述的基于风向湍流强度的风电机组叶片变桨调节装置，其特征在于，所述当前变桨角度分析模块中，所述最佳变桨角度计算模型的表达公式为：9. The wind turbine blade pitch adjustment device based on wind direction turbulence intensity according to claim 8 is characterized in that, in the current pitch angle analysis module, the expression formula of the optimal pitch angle calculation model is: ； ; 式中，表示最佳变桨角度；为风向湍流强度影响系数；为功率影响系数；为历史发电量数据；表示偏移量；In the formula, Indicates the optimal pitch angle; is the wind direction turbulence intensity influence coefficient; is the power influence coefficient; is the historical power generation data; Indicates the offset; 所述变桨角度动态预测模块中，所述变桨角度预测模型的表达公式为：In the pitch angle dynamic prediction module, the pitch angle prediction model is expressed as: ； ; 式中，表示未来时刻可能的预测变桨角度；表示风向湍流变化率系数，用于增强变桨响应的灵敏度；表示瞬时湍流强度变化量；表示湍流强度的平均值；In the formula, Indicates the future The predicted pitch angle possible at any moment; Represents the wind direction turbulence change rate coefficient, which is used to enhance the sensitivity of pitch response; Indicates the instantaneous change in turbulence intensity; represents the average value of turbulence intensity; 所述变桨角度动态预测模块还用于结合多层神经网络模型对所述变桨角度预测模型的特征进行训练，训练后的所述变桨角度预测模型的结构为：The pitch angle dynamic prediction module is also used to train the characteristics of the pitch angle prediction model in combination with a multi-layer neural network model. The structure of the pitch angle prediction model after training is: ； ; 式中，负责输入特征，包括历史数据中的风向湍流强度、风速变化率、发电量波动幅度，形成特征向量集；采用激活函数（ReLU）进行非线性变换；为最终输出的最佳变桨角度。In the formula, Responsible for inputting features, Including wind direction turbulence intensity, wind speed change rate, and power generation fluctuation amplitude in historical data to form a feature vector set; Use activation function (ReLU) for nonlinear transformation; is the optimal pitch angle for the final output. 10.根据权利要求9所述的基于风向湍流强度的风电机组叶片变桨调节装置，其特征在于，所述变桨角度指令调整模块中，通过反馈机制对叶片变桨角度的预测结果进行验证和修正包括：10. The wind turbine blade pitch adjustment device based on wind direction turbulence intensity according to claim 9, characterized in that in the pitch angle instruction adjustment module, verifying and correcting the prediction result of the blade pitch angle through a feedback mechanism comprises: 实时监测所述风电机组的输出功率，与理论最优值对比，若偏差超过设定阈值，则对所述最佳变桨角度计算模型的系数、、参数进行修正；The output power of the wind turbine is monitored in real time and compared with the theoretical optimal value. If the deviation exceeds the set threshold, the coefficient of the optimal pitch angle calculation model is adjusted. , , Parameters are modified; 若风向湍流强度波动超过阈值，则增加变桨调节的灵敏度指标风向湍流变化率系数。If the wind direction turbulence intensity fluctuation exceeds the threshold, the wind direction turbulence change rate coefficient, which is the sensitivity index of pitch adjustment, is increased. .
CN202411884845.1A 2024-12-20 2024-12-20 Wind turbine generator blade pitch-variable adjusting method and device based on wind direction turbulence intensity Active CN119333332B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202411884845.1A CN119333332B (en) 2024-12-20 2024-12-20 Wind turbine generator blade pitch-variable adjusting method and device based on wind direction turbulence intensity

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202411884845.1A CN119333332B (en) 2024-12-20 2024-12-20 Wind turbine generator blade pitch-variable adjusting method and device based on wind direction turbulence intensity

Publications (2)

Publication Number Publication Date
CN119333332A true CN119333332A (en) 2025-01-21
CN119333332B CN119333332B (en) 2025-04-08

Family

ID=94277106

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202411884845.1A Active CN119333332B (en) 2024-12-20 2024-12-20 Wind turbine generator blade pitch-variable adjusting method and device based on wind direction turbulence intensity

Country Status (1)

Country Link
CN (1) CN119333332B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN120431775A (en) * 2025-06-03 2025-08-05 中国民航科学技术研究院 Method, device, storage medium and equipment for determining aircraft landing turbulence index

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011157271A2 (en) * 2010-06-14 2011-12-22 Vestas Wind Systems A/S A method and control unit for controlling a wind turbine in dependence on loading experienced by the wind turbine
EP3214305A1 (en) * 2016-03-04 2017-09-06 Hitachi, Ltd. Control device of plurality of wind turbines and control method of wind farm or plurality of wind turbines
CN108757312A (en) * 2018-06-06 2018-11-06 湘电风能有限公司 A kind of wind-driven generator pitching control method
CN115076029A (en) * 2022-06-28 2022-09-20 国电联合动力技术有限公司 Wind turbine load reduction control method and storage medium based on wind speed and turbulence
CN115563806A (en) * 2022-10-27 2023-01-03 中国船舶重工集团海装风电股份有限公司 Pre-pitch control method, device, equipment and medium
CN118327882A (en) * 2024-04-11 2024-07-12 大唐可再生能源试验研究院有限公司 Large wind turbine yaw deviation identification method based on wind direction turbulence energy

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011157271A2 (en) * 2010-06-14 2011-12-22 Vestas Wind Systems A/S A method and control unit for controlling a wind turbine in dependence on loading experienced by the wind turbine
EP3214305A1 (en) * 2016-03-04 2017-09-06 Hitachi, Ltd. Control device of plurality of wind turbines and control method of wind farm or plurality of wind turbines
CN108757312A (en) * 2018-06-06 2018-11-06 湘电风能有限公司 A kind of wind-driven generator pitching control method
CN115076029A (en) * 2022-06-28 2022-09-20 国电联合动力技术有限公司 Wind turbine load reduction control method and storage medium based on wind speed and turbulence
CN115563806A (en) * 2022-10-27 2023-01-03 中国船舶重工集团海装风电股份有限公司 Pre-pitch control method, device, equipment and medium
CN118327882A (en) * 2024-04-11 2024-07-12 大唐可再生能源试验研究院有限公司 Large wind turbine yaw deviation identification method based on wind direction turbulence energy

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN120431775A (en) * 2025-06-03 2025-08-05 中国民航科学技术研究院 Method, device, storage medium and equipment for determining aircraft landing turbulence index

Also Published As

Publication number Publication date
CN119333332B (en) 2025-04-08

Similar Documents

Publication Publication Date Title
CN119333332B (en) Wind turbine generator blade pitch-variable adjusting method and device based on wind direction turbulence intensity
CN107829878A (en) Yaw control device and method of wind generating set
CN119373658B (en) Wind turbine generator control method and system based on wind farm wind condition prediction
CN119844296A (en) Method and system for dynamically adjusting and controlling fan operation in real time based on environment data
CN118249683A (en) Intelligent excitation control method and system for thermal power units based on autonomous controllable algorithm
CN113294297A (en) Variable weight adjusting method for wind turbine generator nonlinear model prediction torque control
CN119686932A (en) Control method, system and equipment based on operation of wind turbine generator
CN113027674A (en) Control method and device of wind generating set
CN119244438A (en) A direct-drive wind turbine structured identification method and system based on gravitational search algorithm
CN118462479A (en) Control methods, devices, equipment, media and procedures for offshore wind turbines
CN118775147A (en) Wind turbine yaw control method, device, wind power system and storage medium
CN109944740B (en) Wind farm farm group control method and device
CN112943557B (en) Wind power plant, wind generating set and method and equipment for predicting operation state of wind generating set
CN119373660B (en) A method and device for yaw optimization analysis of wind turbine generator set
CN113193572B (en) Method for realizing fan control by modeling wind power frequency modulation parameter probability distribution
CN116816597A (en) Control method and system of wind turbine generator, electronic equipment and storage medium
US11313353B2 (en) Controlling a wind turbine using control outputs at certain time stages over a prediction horizon
CN119717496B (en) Automatic bearing pressing-in force control method
CN119010093B (en) Rapid voltage and frequency adjusting method for integrated virtual power plant and energy storage system
CN120487496A (en) Multi-time scale combined wind farm operation control method, device and equipment
CN114355762B (en) Pitch control method based on nonsingular rapid terminal sliding mode
Chen et al. Active Power Optimization Considering Fatigue Damage in Wind Farms Using Physics-Informed Neural Networks
CN119026944A (en) A method for rapid prediction and optimization of wind farm power and fatigue load based on machine learning
CN120062134A (en) Method, device and equipment for distributing operation power optimization of compressor in compressed air energy storage system
CN119443146A (en) Intelligent equipment control method and system based on device data analysis

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
GR01 Patent grant
GR01 Patent grant