A control method and system for a wind turbine

Abstract

The present disclosure belongs to the technical field of wind power generation, and provides a control method and system of a wind turbine, the method comprising: acquiring strain data of the entire blade on the wind turbine by using a fiber grating sensor; updating the geometric shape of the deformed blade by using a blade, flow field analysis and prediction module according to the strain data of the entire blade; inverting the inflow velocity of the wind turbine according to the geometric shape of the blade, aerodynamic characteristics, wind turbine SCADA data, and the spatial position of the wind turbine in the wind field; predicting the flow field distribution data in the wind field according to the inflow velocity of the wind turbine; and controlling the wind turbine in the wind field according to the flow field distribution data. The problem that the wake control technology in the prior art is mostly based on feedback control and has a certain lag for the natural wind signal with randomness, which is not conducive to achieving the optimal control effect is solved.

Description

Technical Field

[0001] This disclosure pertains to the field of wind power generation technology, and particularly relates to a control method and system for a wind turbine. Background Technology

[0002] A wind turbine is an energy device that converts wind energy into kinetic energy and generates electricity through blades. my country has abundant wind energy resources, and for areas lacking water or fuel resources, or in remote areas, wind power is an ideal source of electricity.

[0003] However, due to the often unstable wind conditions, wind turbines cannot always maintain ideal operating conditions, thus affecting the efficiency of wind power generation, the load on the blades, and their lifespan. Furthermore, for wind turbines in a wind farm, due to the non-linearity of their wind energy capture characteristics, the power loss caused by wake effects on downstream units may prevent the total power generation of the wind farm from reaching its maximum. Turbine wake is closely related to the turbine's operating state; by adjusting the unit's operating parameters and operating status, the intensity and direction of the wake can be changed, thereby reducing wake losses on downstream units, increasing the overall power of the wind farm, and maximizing its efficiency.

[0004] Therefore, in recent years, how to improve the power generation of the entire wind farm through wake control has become one of the important research issues in the field of wind power generation.

[0005] Patent CN114169614A discloses a wind farm optimization scheduling method and system based on wind turbine wake model optimization, belonging to the field of wind farm wake calculation technology. It utilizes SCADA data to analyze and correct the wake model, and combines it with intelligent optimization algorithms to optimize key model parameters with the goal of minimizing wind farm power calculation errors. This allows the proposed wake optimization method to fully consider the actual conditions of the wind farm and customize the model parameters. After optimizing the wake model using this method, the calculation accuracy of the model in actual wind farms can be significantly improved, thus more accurately modeling the wake effect in wind farms. This significantly improves the power prediction accuracy and reliability of wake control strategies, and ultimately, based on the optimized wake model, the overall power generation efficiency and total power output of the wind farm can be improved through wake optimization control and other methods.

[0006] Patent CN115807734A discloses a field-level collaborative control strategy for offshore wind farms based on wake tracking, including a nacelle-type lidar wind measurement system, a wake tracking module, an optimizer, and a field-level controller. First, the nacelle-type lidar wind measurement system measures the raw wind information. Then, the wake tracking module performs wind field inversion to evaluate the parameters of the environmental input wind and identify wake characteristic parameters. Based on an aerodynamic-hydraulic-servo-elastic dynamics simulation model of the offshore wind farm, an intelligent optimization algorithm is used to solve for the optimal wake center position under different environmental conditions, establishing a multi-dimensional intelligent decision database (LUT) and an optimizer. Finally, a field-level collaborative PI controller is used to achieve wake redirection and intelligent control.

[0007] However, existing wake control technologies are mostly based on feedback control, which has a certain lag for natural wind signals with randomness, making it difficult to achieve optimal control results. Summary of the Invention

[0008] To address the aforementioned issues, this disclosure provides a wind turbine control method and system that uses fiber optic grating blade load to monitor wind conditions and predict the wake effect generated by the upstream wind turbine on the downstream, thereby achieving predictive control and realizing ideal control results.

[0009] The following is the technical content of this invention:

[0010] A method for controlling a wind turbine includes:

[0011] Strain data of the entire blade on a wind turbine is acquired using fiber optic grating sensors.

[0012] Based on the strain data of the entire blade, the geometric shape of the deformed blade is obtained;

[0013] The inflow velocity of the wind turbine can be calculated by analyzing the blade geometry, aerodynamic characteristics, SCADA data of the wind turbine, and the spatial position of the wind turbine in the wind field.

[0014] Predict the flow field distribution data in the wind field based on the inflow velocity of the wind turbine;

[0015] The wind turbines in the wind farm are controlled based on the flow field distribution data to maximize the overall power generation of the wind farm.

[0016] Furthermore,

[0017] The method of acquiring strain data for the entire blade of a wind turbine using a fiber optic grating sensor includes:

[0018] Select the root, middle and tip of the blade to install fiber optic grating sensors;

[0019] Based on the measurement results of the fiber Bragg grating sensor, strain data of the entire blade on the wind turbine is collected.

[0020] Furthermore,

[0021] The inflow velocity of the reverse-engineered wind turbine includes:

[0022] Establish and solve the following inverse optimization problem to deduce the inflow velocity V of the wind turbine. rotor :

[0023]

[0024] Among them, F aero For wind turbine loads predicted by the aerodynamic model; F SCADA For wind turbine SCADA data; V cut-in and V cut-out These are the wind speeds at which the wind turbine enters and exits the turbine.

[0025] Furthermore,

[0026] The solution to the optimization inverse problem includes:

[0027] A relationship curve between wind speed and load is established within the wind speed range of the wind turbine operation. The linear solution of the optimization inverse problem is achieved by piecewise linear fitting of the curve.

[0028] Furthermore,

[0029] The linearization solution of the problem achieved through piecewise linear fitting of the curve includes:

[0030] Determine the operating wind speed range for the fan, and clarify the wind speed range during normal operation of the fan.

[0031] During wind turbine operation, wind speed and corresponding load data are collected in real time.

[0032] Based on the collected data, plot the relationship curve between wind speed and load to establish the relationship between wind speed and load;

[0033] The relationship curve between wind speed and load is divided into several segments, and linear fitting is performed on the relationship curve between wind speed and load for each segment.

[0034] By using piecewise linear fitting, the nonlinear relationship between wind speed and load is approximated as a combination of multiple linear segments, thus achieving a linear solution to the optimization inverse problem.

[0035] Furthermore,

[0036] The step of obtaining the deformed blade geometry based on the strain data of the entire blade includes:

[0037] Determine the magnitude and direction of strain at various locations on the blade;

[0038] Based on the geometric shape of the blade, determine the coordinates of each key point;

[0039] Based on the material properties and strain data, the degree of deformation of each part of the blade is calculated using the corresponding mechanical formulas;

[0040] Based on the calculated degree of deformation, the coordinates of each key point are adjusted;

[0041] By connecting the adjusted key points, the geometric shape of the deformed blade is constructed.

[0042] Furthermore,

[0043] The velocity distribution in the wind field is calculated using the following formula:

[0044]

[0045] Among them, C T is the wind turbine thrust coefficient; k is the wake expansion coefficient; R is the wind turbine radius; a rotor V is the wind turbine homogenization induction factor; e is the natural base; p is the wake expansion correction index; V rotor The inflow velocity of the wind turbine.

[0046] Furthermore,

[0047] The formula for calculating the velocity distribution in the wind field is as follows:

[0048] In the wake prediction process, an exponential function is used to simulate the nonlinear expansion of the wake, and the wake expansion correction index p is corrected based on wind field detection data using the least squares method.

[0049] Furthermore,

[0050] The control of wind turbines in the wind farm based on flow field distribution data to maximize the overall power generation of the wind farm includes:

[0051] By controlling the yaw angle of each wind turbine in the wind farm, the power of all wind turbines in the wind farm is maximized.

[0052] Its control objective function is:

[0053]

[0054] In the formula, t represents time, T represents total time; n represents the wind turbine number; N represents the total number of wind turbines in the wind farm; θ yaw For the yaw angle of the wind turbine, Let n be the power of the wind turbine n at time t.

[0055]

[0056] ρ is the air density, A is the swept area, V is the velocity in the wind field, and C is the air density. p The efficiency power coefficient.

[0057] A control system for a wind turbine, characterized in that it comprises:

[0058] The measurement module is used to acquire strain data of the entire blade on the wind turbine using fiber Bragg grating sensors;

[0059] The geometry construction module is used to obtain the deformed blade geometry based on the strain data of the entire blade.

[0060] The inflow velocity calculation module is used to calculate the inflow velocity of the wind turbine based on the blade geometry, aerodynamic characteristics, wind turbine SCADA data, and the spatial position of the wind turbine in the wind field.

[0061] The flow field distribution calculation module is used to predict the flow field distribution data in the wind field based on the inflow velocity of the wind turbine.

[0062] The control module is used to control the wind turbines in the wind farm based on the flow field distribution data, so as to maximize the overall power generation of the wind farm.

[0063] Compared with the prior art, this disclosure has the following advantages:

[0064] This invention utilizes fiber optic grating sensors to acquire strain data of the entire blade on a wind turbine, thereby obtaining the deformed blade geometry. By combining the aerodynamic characteristics of the blade, wind turbine SCADA data, and the spatial position of the wind turbine in the wind farm, the inflow velocity of the wind turbine can be calculated. Then, the flow field distribution data in the wind farm is predicted based on the inflow velocity, and finally, the wind turbines in the wind farm are controlled based on the flow field distribution data to maximize the overall power generation of the wind farm.

[0065] In this process, fiber optic grating sensors can perceive the strain of the blades in real time and accurately, thus reflecting changes in wind conditions promptly. By accurately acquiring the blade geometry and analyzing data from multiple sources, a more comprehensive and accurate understanding of the real-time state of the wind field can be achieved, allowing for early prediction of future airflow conditions. This enables more timely and effective adjustments to the wind turbine's operating status, better enabling it to cope with the randomness of natural wind signals and achieving optimal control. This solves the problem of unsatisfactory power generation caused by feedback control lag in existing wake control technologies.

[0066] Other features and advantages of this disclosure will be set forth in the following description and will be apparent in part from the description or may be learned by practicing the disclosure. The objects and other advantages of this disclosure may be realized and obtained by means of the structures pointed out in the description, claims and drawings. Attached Figure Description

[0067] To more clearly illustrate the technical solutions in the embodiments of this disclosure or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this disclosure. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0068] Figure 1 A schematic diagram of a wind farm group control method based on wind condition inversion through fiber Bragg grating blade load monitoring is shown.

[0069] Figure 2 A schematic diagram of the arrangement of the light grating sensor is shown. Detailed Implementation

[0070] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this disclosure, and not all embodiments. Based on the embodiments of this disclosure, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this disclosure.

[0071] Figure 1 A schematic diagram of the method according to the present invention is shown. Specific implementation details of the present invention include:

[0072] Step 1: Use the fiber optic strain measurement module and fiber optic grating sensors to measure the strain of the entire blade on all wind turbines in the wind field.

[0073] principle:

[0074] A fiber Bragg grating sensor is an optical device that uses a grating-like structure with periodic refractive index variations fabricated inside an optical fiber. When light passes through the fiber Bragg grating sensor, light of a specific wavelength is reflected back, and this reflected wavelength is affected by external physical quantities (such as strain).

[0075] When wind turbine blades are strained, the fiber Bragg grating elongates or compresses, causing changes in its period and refractive index. This results in a corresponding change in the wavelength of the reflected light.

[0076] The fiber optic strain measurement module can accurately measure the strain generated by the blade by detecting changes in the wavelength of the reflected light.

[0077] Arrangement method:

[0078] Fiber Bragg grating sensors are installed at key locations such as the blade root, middle, and tip. During installation, it is crucial to ensure a tight fit between the sensor and the blade surface without affecting the blade's normal structure and performance. Fiber Bragg grating sensors can be glued to the blade surface; however, for actual wind turbine blade production, they can also be embedded within the blade's ply during manufacturing.

[0079] Step 2: Based on the measurement results of the fiber Bragg grating sensor, collect the blade strain data of all wind turbines in the wind farm, and update the deformed blade geometry based on the blade strain data.

[0080] Step two specifically includes:

[0081] The collected strain data were analyzed in detail to determine the magnitude and direction of strain at various locations on the blade.

[0082] Based on the material properties and strain data, the degree of deformation of each part of the blade is calculated using the corresponding mechanical formulas;

[0083] Based on the geometric shape of the blade, determine the coordinates of each key point;

[0084] Based on the calculated degree of deformation, the coordinates of these key points are adjusted. For example, if a part undergoes tensile deformation, the coordinate values ​​of the key points in that part are increased accordingly; if compressive deformation occurs, the coordinate values ​​are decreased.

[0085] By connecting the adjusted key points, the geometric profile of the deformed blade is constructed.

[0086] Step 3: Combining the updated blade geometry and aerodynamic characteristics, wind turbine SCADA data, and the spatial location of wind turbines in the wind farm, the inflow velocity V of each wind turbine is inverted. rotor And based on the inflow velocity V of the wind turbine rotor Predict the flow field distribution in a wind field.

[0087] SCADA data for wind turbines refers to the load data of the wind turbine monitored by SCADA, such as the torque and thrust of the wind turbine rotor.

[0088] Specifically, the flow field distribution data includes:

[0089] (1) Wind speed distribution: the magnitude and direction of wind speed at different locations.

[0090] (2) Turbulence intensity distribution: The distribution of parameters that reflects the degree of airflow turbulence in the wind field.

[0091] Specifically, the following optimization inverse problem is established and solved to derive the inflow velocity V of each wind turbine. rotor :

[0092]

[0093] Among them, F aero For wind turbine loads predicted by the aerodynamic model; F SCADA This refers to SCADA data for wind turbines, specifically the wind turbine load collected by on-site monitoring equipment; V cut-in and V cut-out These are the wind speeds at which the wind turbine enters and exits the turbine.

[0094] The above optimization inverse problem can be solved using heuristic algorithms or linear acceleration methods.

[0095] When using a linear acceleration solution method, it is necessary to establish a relationship curve between wind speed and load within the wind speed range of the wind turbine operation, and achieve linear solution of the problem by piecewise linear fitting of the curve.

[0096] Specifically, it includes:

[0097] (1) Determine the operating wind speed range of the fan: Clarify the wind speed range during normal operation of the fan.

[0098] (2) Data collection: During the operation of the wind turbine, wind speed and corresponding load data are collected in real time.

[0099] (3) Establish wind-load curve: Based on the collected data, draw the relationship curve between wind speed and load to show the relationship between wind speed and load.

[0100] (4) Piecewise linear fitting: Divide the wind speed vs. load curve into several segments and perform linear fitting on each segment. This can be achieved using mathematical methods or relevant software tools.

[0101] (5) Linearization solution: By piecewise linear fitting, the originally nonlinear wind speed-load relationship is approximated as a combination of multiple linear segments, thereby realizing the linearization solution of the problem.

[0102] Specifically, in the flow field distribution within a wind field, the velocity distribution is calculated using the following formula:

[0103]

[0104] Among them, C T is the wind turbine thrust coefficient; k is the wake expansion coefficient; R is the wind turbine radius; a rotor is the wind turbine homogenization induction factor; e is the natural base; p is the wake expansion correction index.

[0105] Step 4: The wind turbine control module performs predictive control of the wind turbines in the wind farm according to the given control objectives, based on the flow field distribution data in the wind farm.

[0106] Specifically, by controlling the yaw angle of each wind turbine in the wind farm, the power of all wind turbines in the wind farm is maximized. The objective function is:

[0107]

[0108]

[0109] In the formula, t represents time, T represents total time; n represents the wind turbine number; N represents the total number of wind turbines in the wind farm; θ yaw For the yaw angle of the wind turbine, Let ρ be the power of wind turbine n at time t, ρ be the air density, A be the swept area, V be the velocity in the wind field, and C be the velocity of the wind turbine n at time t. p The efficiency power coefficient.

[0110] Note:

[0111] When the prediction difference between two consecutive time steps is less than or equal to 5%, the method of this invention is used for prediction; when the prediction difference between two consecutive time steps is greater than 5%, the traditional control method is used for prediction.

[0112] When the inflow turbulence intensity is less than 5%, the aerodynamic load is solved using the classical momentum blade element theory; when the inflow turbulence intensity is greater than 5%, the momentum blade element theory model needs to be dynamically corrected for inflow.

[0113] Based on the method of the present invention, this disclosure also provides a prediction system corresponding to the above method, which includes:

[0114] The system comprises the following modules: a measurement module for acquiring strain data of the entire blade of a wind turbine using fiber optic grating sensors; a geometry construction module for acquiring the deformed blade geometry based on the strain data of the entire blade; an inflow velocity calculation module for calculating the inflow velocity of the wind turbine based on the blade geometry, aerodynamic characteristics, wind turbine SCADA data, and the spatial position of the wind turbine in the wind farm; a flow field distribution calculation module for predicting the flow field distribution data in the wind farm based on the inflow velocity of the wind turbine; and a control module for controlling the wind turbines in the wind farm based on the flow field distribution data to maximize the overall power generation of the wind farm.

[0115] Although the present disclosure has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A control method for a wind turbine, characterized in that, include: Strain data of the entire blade on a wind turbine is acquired using fiber optic grating sensors. Based on the strain data of the entire blade, the geometric shape of the deformed blade is obtained; The inflow velocity of the wind turbine can be calculated by analyzing the blade geometry, aerodynamic characteristics, SCADA data of the wind turbine, and the spatial position of the wind turbine in the wind field. Predict the flow field distribution data in the wind field based on the inflow velocity of the wind turbine; The wind turbines in the wind farm are controlled based on the flow field distribution data to maximize the overall power generation of the wind farm. The inflow velocity of the reverse-engineered wind turbine includes: Establish and solve the following inverse optimization problem to deduce the inflow velocity of the wind turbine. : in, The wind turbine load is predicted by the aerodynamic model; For wind turbine SCADA data; and These are the wind speeds at which the wind turbine enters and exits the turbine. The solution to the optimization inverse problem includes: A relationship curve between wind speed and load is established within the wind speed range of wind turbine operation. The linear solution of the optimization inverse problem is achieved by piecewise linear fitting of the curve. The linearization solution of the problem achieved through piecewise linear fitting of the curve includes: Determine the operating wind speed range for the fan, and clarify the wind speed range during normal operation of the fan. During wind turbine operation, wind speed and corresponding load data are collected in real time. Based on the collected data, plot the relationship curve between wind speed and load to establish the relationship between wind speed and load; The relationship curve between wind speed and load is divided into several segments, and linear fitting is performed on the relationship curve between wind speed and load for each segment. By using piecewise linear fitting, the nonlinear relationship between wind speed and load is approximated as a combination of multiple linear segments, thus achieving a linear solution to the optimization inverse problem.

2. The control method for a wind turbine according to claim 1, characterized in that, The method of acquiring strain data for the entire blade of a wind turbine using a fiber optic grating sensor includes: Select the root, middle and tip of the blade to install fiber optic grating sensors; Based on the measurement results of the fiber Bragg grating sensor, strain data of the entire blade on the wind turbine is collected.

3. The control method for a wind turbine according to claim 1, characterized in that, The geometric shape of the deformed blade is obtained based on the strain data of the entire blade. include: Determine the magnitude and direction of strain at various locations on the blade; Based on the geometric shape of the blade, determine the coordinates of each key point; Based on the material properties and strain data, the degree of deformation of each part of the blade is calculated using the corresponding mechanical formulas; Based on the calculated degree of deformation, the coordinates of each key point are adjusted; By connecting the adjusted key points, the geometric shape of the deformed blade is constructed.

4. The control method for a wind turbine according to claim 1, characterized in that, The control of wind turbines in the wind farm based on flow field distribution data to maximize the overall power generation of the wind farm includes: By controlling the yaw angle of each wind turbine in the wind farm, the power of all wind turbines in the wind farm is maximized. Its control objective function is: In the formula, t is time, T is total time; n is the wind turbine number; N is the total number of wind turbines in the wind farm; For the yaw angle of the wind turbine, Let n be the power of the wind turbine n at time t. air density, For swept area, The speed in the wind field, The efficiency power coefficient.

5. A control system for a wind turbine, characterized in that, A control method for implementing the wind turbine according to any one of claims 1-4, comprising: The measurement module is used to acquire strain data of the entire blade on the wind turbine using fiber Bragg grating sensors; The geometry construction module is used to obtain the deformed blade geometry based on the strain data of the entire blade. The inflow velocity calculation module is used to calculate the inflow velocity of the wind turbine based on the blade geometry, aerodynamic characteristics, wind turbine SCADA data, and the spatial position of the wind turbine in the wind field. The flow field distribution calculation module is used to predict the flow field distribution data in the wind field based on the inflow velocity of the wind turbine. The control module is used to control the wind turbines in the wind farm based on the flow field distribution data, so as to maximize the overall power generation of the wind farm.

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