Control method and system of wind turbine

Abstract

The invention belongs to the technical field of wind power generation, and provides a control method and system for a wind turbine, and the method comprises the steps: obtaining the strain data of a whole blade on the wind turbine through a fiber grating sensor; according to the strain data of the whole blade, using the blade and a flow field analysis and prediction module to update the geometric shape of the deformed blade; inversing the inflow velocity of the wind turbine according to the geometric shape and aerodynamic characteristics of the blade, the SCADA data of the wind turbine and the spatial position of the wind turbine in the wind field; predicting flow field distribution data in the wind field according to the inflow velocity of the wind turbine; and controlling the wind turbines in the wind field according to the flow field distribution data. The problems that in the prior art, most wake flow control technologies are based on feedback control, certain lag exists for random natural wind signals, and the optimal control effect cannot be achieved easily are solved.

Description

Technical Field

[0001] The present disclosure belongs to the technical field of wind power generation, and in particular, relates to a control method and system for a wind turbine. Background Art

[0002] A wind turbine is an energy device that converts wind energy into kinetic energy through blades and generates electricity. my country is rich in wind energy resources. Wind power generation is an ideal source of electricity for areas that lack resources such as water or fuel or are remote.

[0003] However, since wind levels are often unstable, it is difficult for wind turbines to always maintain an ideal working condition, which affects the efficiency of wind power generation and the load and life of the blades. In addition, for wind turbines in wind farms, due to the nonlinearity of wind energy capture characteristics of wind turbines, the power loss caused by the wake of downstream units may make the total power generation of the wind farm not the maximum. The wind turbine wake is closely related to the operating status of the wind turbine. By adjusting the operating parameters and operating status of the unit, the wake intensity and direction can be changed, thereby reducing the wake loss of the downstream units, increasing the overall power of the wind farm, and maximizing the benefits of the wind farm.

[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] The patent with publication number CN114169614A discloses a wind farm optimization scheduling method and system based on the optimization of the wind turbine wake model, which belongs to the field of wind farm wake calculation technology. SCADA data is used to parse the correction work of the wake model, and combined with the intelligent optimization algorithm, the key parameters of the model are optimized with the goal of minimizing the wind farm power calculation error, so that the wake optimization method proposed by this technology can fully consider the actual situation of the wind farm to customize the parameters of the model. After optimizing the wake model by this method, the calculation accuracy of the model in the actual wind farm can be greatly improved, so as to more accurately model the wake effect in the wind farm, which can significantly improve the power prediction accuracy of the wind farm and the reliability of the wake control strategy, and then based on the optimized wake model, the overall power generation efficiency and overall power generation of the wind farm can be improved through wake optimization control and other methods.

[0006] The patent with publication number CN115807734A discloses a field-level collaborative control strategy for offshore wind farms based on wake tracking, including a nacelle-type laser radar wind measurement system, a wake tracking module, an optimizer, and a field-level controller. First, the original wind information is measured using the nacelle-type laser radar wind measurement system, and the wind field inversion is performed through the wake tracking module to complete the parameter evaluation of the environmental input wind and the identification of the wake characteristic parameters; based on the offshore wind farm pneumatic-hydraulic-servo-elastic dynamics simulation model, the optimal wake center position under different environmental conditions is solved through an intelligent optimization algorithm, and a multi-dimensional intelligent decision database LUT and optimizer are established; the 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 random natural wind signals and is not conducive to achieving the optimal control effect. Summary of the invention

[0008] To solve the above problems, the present disclosure provides a control method and system for a wind turbine, which uses fiber grating blade load to monitor wind conditions and predict the wake effect of upstream wind turbines on downstream wind turbines to perform predictive control, thereby achieving ideal control effects.

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

[0010] A method for controlling a wind turbine, comprising:

[0011] Use fiber grating sensors to obtain strain data of the entire blade on a wind turbine;

[0012] According to the strain data of the entire blade, the geometric shape of the blade after deformation is obtained;

[0013] Inversely derive the wind turbine inflow velocity based on blade geometry, aerodynamic characteristics, wind turbine SCADA data, and the spatial position of the wind turbine in the wind farm;

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

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

[0016] Further,

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

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

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

[0020] Further,

[0021] The inverse performance of the inflow velocity of the wind turbine includes:

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

[0023]

[0024] Among them, F aero The wind turbine load predicted by the aerodynamic model; F SCADA is the wind turbine SCADA data; V cut-in and V cut-out are the cut-in and cut-out wind speeds of the wind turbine, respectively.

[0025] Further,

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

[0027] A relationship curve between wind speed and load is established within the wind speed range of the fan, and a linear solution to the optimization inverse problem is achieved through piecewise linear fitting of the curve.

[0028] Further,

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

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

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

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

[0033] The relationship curve between wind speed and load is divided into several sections, and a linear fit is performed on each section of the relationship curve between wind speed and load;

[0034] Through piecewise linear fitting, the nonlinear relationship between wind speed and load is approximately expressed as a combination of multiple linear segments, thereby achieving a linear solution to the inverse optimization problem.

[0035] Further,

[0036] The step of obtaining the geometric shape of the deformed blade according to the strain data of the entire blade comprises:

[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] According to the material properties and strain data, the corresponding mechanical formula is used to calculate the deformation degree of each part of the blade;

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

[0041] By connecting the adjusted key points, the deformed blade geometry is constructed.

[0042] Further,

[0043] In the flow field distribution in the wind field, the velocity distribution calculation formula in the wind field is:

[0044]

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

[0046] Further,

[0047] The calculation formula for the velocity distribution in the wind field is:

[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 by the least squares method based on the wind field detection data.

[0049] Further,

[0050] The controlling of the wind turbines in the wind farm according to the flow field distribution data so as 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] Where 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; θ yaw is the yaw angle of the wind turbine, is the power of wind turbine n at time t,

[0055]

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

[0057] A control system for a wind turbine, characterized by comprising:

[0058] A measurement module, used for acquiring strain data of the entire blade of the wind turbine using a fiber grating sensor;

[0059] A geometric shape building module is used to obtain the geometric shape of the blade after deformation according to the strain data of the entire blade;

[0060] The inflow velocity calculation module is used to invert 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;

[0061] A flow field distribution calculation module is used to predict the flow field distribution data in the wind field according to the inflow velocity of the wind turbine;

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

[0063] Compared with the prior art, the present invention has the following advantages:

[0064] The present invention obtains the strain data of the entire blade on the wind turbine by using a fiber grating sensor, and obtains the geometric shape of the deformed blade based on this. At the same time, the inflow velocity of the wind turbine is inverted by combining the aerodynamic characteristics of the blade, the wind turbine SCADA data and the spatial position of the wind turbine in the wind farm. Then, the flow field distribution data in the wind farm is predicted according to the inflow velocity, and finally the wind turbine in the wind farm is controlled according to the flow field distribution data, so that the overall power generation of the wind farm is maximized.

[0065] In this process, the fiber grating sensor can sense the strain of the blades in real time and accurately, thus reflecting the changes in wind conditions in a timely manner; by accurately acquiring the geometric shape of the blades and analyzing comprehensive data from various aspects, it can more comprehensively and accurately understand the real-time state of the wind farm and predict the future state of the airflow in the wind farm in advance. The operating state of the wind turbine can be adjusted more timely and effectively, so that it can better cope with the randomness of natural wind signals, thereby achieving the optimal control effect, solving the problem of unsatisfactory power generation caused by feedback control lag in existing wake control technology.

[0066] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

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

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

[0070] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

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

[0072] Step 1: Use a fiber Bragg grating strain measurement module to measure the strain of the entire blades of all wind turbines in the wind farm using a fiber Bragg grating sensor.

[0073] principle:

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

[0075] When the wind turbine blade is strained, it will cause the fiber Bragg grating to stretch or compress, resulting in changes in the period and refractive index of the grating, which will cause the wavelength of the reflected light to change accordingly.

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

[0077] Layout:

[0078] Select key locations such as the root, middle, and tip of the blade to install the fiber Bragg grating sensor. During the installation process, ensure that the sensor fits tightly against the blade surface and does not affect the normal structure and performance of the blade. The fiber Bragg grating sensor can be attached to the blade surface. If used in the production of actual wind turbine blades, it can also be laid inside the blade ply during the production process.

[0079] Step 2: According to the measurement results of the fiber grating sensor, the blade strain data of all wind turbines in the wind farm are collected, and the geometric shape of the deformed blades is updated according to the blade strain data.

[0080] Step 2 specifically includes:

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

[0082] According to the material properties and strain data, the corresponding mechanical formula is used to calculate the deformation degree of each part of the blade;

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

[0084] According to the calculated deformation degree, the coordinates of these key points are adjusted. For example, if a part is stretched, the coordinate value of the key point of the part is increased accordingly; if a compression deformation occurs, the coordinate value is reduced.

[0085] By connecting the adjusted key points, the deformed blade geometry is constructed.

[0086] Step 3: Invert the inflow velocity V of each wind turbine based on the updated blade geometry, aerodynamic characteristics, wind turbine SCADA data, and the spatial position of the wind turbine in the wind farm. rotor , and based on the inflow velocity V of the wind turbine rotor Predict flow distribution in a wind farm.

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

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

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

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

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

[0092]

[0093] Among them, F aero The wind turbine load predicted by the aerodynamic model; F SCADA is the wind turbine SCADA data, specifically the wind turbine load collected by the on-site monitoring equipment; V cut-in and V cut-out are the cut-in and cut-out wind speeds of the wind turbine, respectively.

[0094] The above inverse optimization problem can be solved by a heuristic algorithm solution or a linear acceleration solution.

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

[0096] Specifically include:

[0097] (1) Determine the 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 a curve of wind speed and load to show the relationship between wind speed and load.

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

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

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

[0103]

[0104] Among them, C T is the wind rotor thrust coefficient; k is the wake expansion coefficient; R is the wind rotor radius; a rotor is the wind wheel 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 on the wind turbines in the wind farm according to a given control target based on the flow field distribution data in the wind farm.

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

[0107]

[0108]

[0109] Where 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; θ yaw is the yaw angle of the wind turbine, is the power of wind turbine n at time t, ρ is the air density, A is the swept area, V is the speed in the wind field, C p is the efficiency power coefficient.

[0110] Note:

[0111] When the prediction difference between the previous and next time steps is less than or equal to 5%, the prediction method of the present invention is adopted; when the prediction difference between the previous and next time steps is greater than 5%, the traditional control method is adopted for prediction.

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

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

[0114] The measuring module is used to obtain the strain data of the entire blade on the wind turbine using the fiber grating sensor; the geometric shape construction module is used to obtain the geometric shape of the deformed blade according to the strain data of the entire blade; the inflow velocity calculation module is used to invert the inflow velocity of the wind turbine according to the blade geometry, aerodynamic characteristics, wind turbine SCADA data, and the spatial position of the wind turbine in the wind farm; the flow field distribution calculation module is used to predict the flow field distribution data in the wind farm according to the inflow velocity of the wind turbine; the control module is used to control the wind turbine in the wind farm according to 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 aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A method for controlling a wind turbine, characterized in that: include: Use fiber grating sensors to obtain strain data of the entire blade on a wind turbine; According to the strain data of the entire blade, the geometric shape of the blade after deformation is obtained; Inversely derive the wind turbine inflow velocity based on blade geometry, aerodynamic characteristics, wind turbine SCADA data, and the spatial position of the wind turbine in the wind farm; Predict the flow field distribution data in the wind field according to the inflow velocity of the wind turbine; The wind turbines in the wind farm are controlled according to the flow field distribution data to maximize the overall power generation of the wind farm.

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

3. A wind turbine control method according to claim 1, characterized in that: The inverse performance of the inflow velocity of the wind turbine includes: Establish and solve the following inverse optimization problem to inversely calculate the inflow velocity V of the wind turbine: rotor : Among them, F aero The wind turbine load predicted by the aerodynamic model; F SCADA is the wind turbine SCADA data; V cut-in and V cut-out are the cut-in and cut-out wind speeds of the wind turbine, respectively.

4. A wind turbine control method according to claim 3, characterized in that: The solution of the inverse optimization problem includes: A relationship curve between wind speed and load is established within the wind speed range of the fan, and a linear solution to the optimization inverse problem is achieved through piecewise linear fitting of the curve.

5. A wind turbine control method according to claim 4, characterized in that: The linear solution of the problem achieved by piecewise linear fitting of the curve includes: Determine the wind speed range of the fan operation and clarify the wind speed range during normal operation of the fan. During the operation of the fan, the wind speed and corresponding load data are collected in real time. Based on the collected data, draw the relationship curve between wind speed and load, and establish the relationship between wind speed and load; The relationship curve between wind speed and load is divided into several sections, and a linear fit is performed on each section of the relationship curve between wind speed and load; Through piecewise linear fitting, the nonlinear relationship between wind speed and load is approximately expressed as a combination of multiple linear segments, thereby achieving a linear solution to the inverse optimization problem.

6. A wind turbine control method according to claim 1, characterized in that: The step of obtaining the geometric shape of the deformed blade according to 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; According to the material properties and strain data, the corresponding mechanical formula is used to calculate the deformation degree of each part of the blade; According to the calculated deformation degree, the coordinates of each key point are adjusted; By connecting the adjusted key points, the deformed blade geometry is constructed.

7. A wind turbine control method according to claim 1, characterized in that: In the flow field distribution in the wind field, the velocity distribution calculation formula in the wind field is: Among them, C T is the wind rotor thrust coefficient; k is the wake expansion coefficient; R is the wind rotor radius; a rotor is the wind wheel homogenization induction factor; e is the natural base; p is the wake expansion correction index, V rotor is the inflow velocity of the wind turbine.

8. A method for controlling a wind turbine according to claim 4, characterized in that: The calculation formula for the velocity distribution in the wind field is: 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 by the least squares method based on the wind field detection data.

9. A method for controlling a wind turbine according to claim 7, characterized in that: The controlling of the wind turbines in the wind farm according to the flow field distribution data so as 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: Where 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; θ yaw is the yaw angle of the wind turbine, is the power of wind turbine n at time t, ρ is air density, A is the swept area, V is the speed in the wind field, C p is the efficiency power factor.

10. A control system for a wind turbine, characterized in that: include: A measurement module, used for acquiring strain data of the entire blade of the wind turbine using a fiber grating sensor; A geometric shape building module is used to obtain the geometric shape of the blade after deformation according to the strain data of the entire blade; The inflow velocity calculation module is used to invert 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 is used to predict the flow field distribution data in the wind field according to the inflow velocity of the wind turbine; The control module is used to control the wind turbines in the wind farm according to the flow field distribution data so as to maximize the overall power generation of the wind farm.

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