Intelligent correction control method and system for wind turbine generator of wind power plant

Abstract

The invention provides an intelligent correction control method and system for a wind turbine generator of a wind power plant. The method comprises the steps that incoming flow wind information of the environment where the wind power plant is located and operation parameter information of the wind turbine generator of the wind power plant are obtained through a laser radar; determining a yaw correction control quantity according to the incoming wind information; determining an optimal gain value and an optimal pitch angle of the wind turbine generator according to the operation parameter information of the wind turbine generator of the wind power plant; determining a target correction strategy of the wind turbine generator according to the yaw correction control quantity and the optimal gain value and the optimal pitch angle of the wind turbine generator; and controlling the operation state of the wind turbine generator according to the target correction strategy. According to the scheme of the invention, by integrating yaw correction, gain self-optimization and pitch angle optimization, the wind turbine generator can realize dynamic optimal control in a complex environment.

Description

Technical Field

[0001] This application relates to the technical field of wind power generation control, and particularly to an intelligent correction control method and system for wind turbines in a wind farm. Background Art

[0002] Wind power generation belongs to renewable energy and clean energy. Wind power generation is an important form of wind energy utilization. Wind energy is a renewable, pollution-free, high-energy, and broad-prospect energy source. The correction control technology for wind farms has emerged, and its appearance plays an important role in improving the performance and stability of wind energy power generation systems and the utilization efficiency of wind energy.

[0003] In the prior art, the wind direction measurement accuracy of the wind vane is affected by the impeller, and deviations in calibration, unreasonable yaw actions, etc. will all cause the wind turbine to have a wind alignment deviation, thus affecting the power generation performance of the wind turbine. Summary of the Invention

[0004] This application provides an intelligent correction control method and system for wind turbines in a wind farm. By integrating yaw correction, gain self-optimization, and pitch angle optimization, the wind turbine can achieve dynamic optimal control in a complex environment.

[0005] To solve the above technical problems, the technical solution of this application is as follows:

[0006] An intelligent correction control method for wind turbines in a wind farm, comprising:

[0007] Obtaining the oncoming wind information of the environment where the wind farm is located and the operation parameter information of the wind turbines in the wind farm through a lidar;

[0008] Determining a yaw correction control amount according to the oncoming wind information;

[0009] Determining the optimal gain value and the optimal pitch angle of the wind turbine according to the operation parameter information of the wind turbines in the wind farm;

[0010] Determining the target correction strategy of the wind turbine according to the yaw correction control amount, the optimal gain value of the wind turbine, and the optimal pitch angle;

[0011] Controlling the operation state of the wind turbine according to the target correction strategy.

[0012] Optionally, obtaining the oncoming wind information of the environment where the wind farm is located through a lidar, comprising:

[0013] Obtaining the reflection signal of the detection laser when it hits the suspended particles in the atmosphere by the detection laser emitted into the atmosphere by the lidar installed on the top of the nacelle;

[0014] Analyzing and processing the reflection signal to determine the oncoming wind information.

[0015] Optionally, analyze and process the reflected signal to determine the oncoming wind information, including:

[0016] Mix the reflected signal with the local oscillator light to generate an intermediate frequency signal, extract the frequency shift through Fourier transform, and obtain the Doppler frequency shift extraction value;

[0017] Determine the oncoming wind information based on the Doppler frequency shift extraction value and the laser wavelength.

[0018] Optionally, determine the yaw correction control amount according to the oncoming wind information, including:

[0019] Determine the wind turbine alignment deviation based on the difference between the wind direction in the oncoming wind information and the nacelle yaw angle;

[0020] Obtain the yaw correction control amount based on the wind turbine alignment deviation.

[0021] Optionally, determine the optimal gain value of the wind turbine according to the operating parameter information of the wind turbines in the wind farm, including:

[0022] Determine the blade contamination state of the wind turbine according to the real-time operating parameter information of the wind turbines in the wind farm and the air density;

[0023] Determine the optimal gain value of the wind turbine according to the blade contamination state.

[0024] Optionally, determine the optimal pitch angle of the wind turbine according to the operating parameter information of the wind turbines in the wind farm, including:

[0025] According to the actual power coefficient operating parameter information of the wind turbines in the wind farm;

[0026] Determine the aerodynamic efficiency decay index according to the actual power coefficient operating parameter information;

[0027] Determine the optimal pitch angle of the wind turbine according to the aerodynamic efficiency decay index.

[0028] Optionally, determine the target correction strategy of the wind turbine according to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine, including:

[0029] According to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine, determine the target correction strategy of the wind turbine according to the objective function Determine the target correction strategy of the wind turbine; where P gen is the power generation, γ 1 、γ 2 , M r is the blade bending moment, ω y is the yaw motor speed, ω ois the ideal rotational speed.

[0030] An embodiment of the present invention further provides an intelligent correction control system for a wind turbine in a wind farm, including:

[0031] An acquisition module, configured to acquire the oncoming wind information of the environment where the wind farm is located and the operation parameter information of the wind turbines in the wind farm through a lidar;

[0032] A processing module, configured to determine a yaw correction control amount according to the oncoming wind information; determine the optimal gain value and the optimal pitch angle of the wind turbine according to the operation parameter information of the wind turbines in the wind farm; determine the target correction strategy of the wind turbine according to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine;

[0033] A control module, configured to control the operation state of the wind turbine according to the target correction strategy.

[0034] An embodiment of the present invention further provides a computing device, including: one or more processors; a storage device, configured to store one or more programs, and when the one or more programs are executed by the one or more processors, enable the one or more processors to implement the method as described above.

[0035] An embodiment of the present invention further provides a computer-readable storage medium for a computing device, where a program is stored in the computer-readable storage medium, and when the program is executed by a processor, the method as described above is implemented.

[0036] The above technical solution of the present application has at least the following technical effects:

[0037] The above solution of the present application acquires the oncoming wind information of the environment where the wind farm is located and the operation parameter information of the wind turbines in the wind farm through a lidar; determines a yaw correction control amount according to the oncoming wind information; determines the optimal gain value and the optimal pitch angle of the wind turbine according to the operation parameter information of the wind turbines in the wind farm; determines the target correction strategy of the wind turbine according to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine; controls the operation state of the wind turbine according to the target correction strategy. By integrating yaw correction, gain self-optimization and pitch angle optimization, the wind turbine can achieve dynamic optimal control in a complex environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 is a schematic flow chart of an intelligent correction control method for a wind turbine in a wind farm provided by an embodiment of the present application;

[0039] Figure 2 is a schematic module diagram of an intelligent correction control system for a wind turbine in a wind farm provided by an embodiment of the present application. Detailed implementation manners

[0040] To make the objectives, technical solutions, and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below. Apparently, the described embodiments are some, but not all, of the embodiments of the present application. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present application without creative efforts also belong to the scope of protection of the present application.

[0041] As Figure 1 shown, an intelligent correction control method for a wind turbine in a wind farm provided by an embodiment of the present application includes:

[0042] Step 11: Obtain the oncoming wind information of the environment where the wind farm is located and the operation parameter information of the wind turbines in the wind farm through a lidar; the operation parameter information of the wind turbines includes: obtaining the fouling state and pitch angle of the blades of the wind turbines in the wind farm. Of course, it may further include: power output, pitch angle, yaw angle, generator speed, torque (inverter data), vibration, temperature (condition monitoring system), etc. collected by a generator sensor.

[0043] Step 12: Determine a yaw correction control amount according to the oncoming wind information;

[0044] Step 13: Determine the optimal gain value and optimal pitch angle of the wind turbines according to the operation parameter information of the wind turbines in the wind farm;

[0045] Step 14: Determine the target correction strategy of the wind turbines according to the yaw correction control amount, the optimal gain value, and the optimal pitch angle of the wind turbines;

[0046] Step 15: Control the operation state of the wind turbines according to the target correction strategy.

[0047] In this embodiment of the present application, by integrating yaw correction, gain self-optimization, and pitch angle optimization, the wind turbines can achieve dynamic optimal control in a complex environment. Specifically, a lidar emits laser with a specific wavelength to actively detect the reflection signal of the laser when it hits the "particles" suspended in the atmosphere (including tiny liquid and solid particles, such as water vapor, pollen, dust, etc.). By analyzing the Doppler frequency shift of the laser and then analyzing through signal processing and other algorithms, accurate and reliable oncoming wind information can be obtained. The lidar measures the wind speed and direction without being affected by the impeller disturbance. The wind deviation of the unit is obtained through the wind deviation optimization module, and accurate wind alignment is achieved through intelligent yaw control, reducing the power generation loss caused by wind alignment deviation and increasing the annual power generation by 0.3% - 2%.

[0048] Blade contamination, air density changes, etc. can all cause the wind turbine to fail to track the optimal tip speed ratio, thus affecting power generation performance. "Control gain self-optimization" aims at energy optimization, online analyzes the operation data of the unit to obtain the optimal gain value, and dynamically adjusts in real time following the change of air density to ensure that the unit can still operate in the optimal state under different blade surface fouling conditions and different air densities. By adopting this technology, the power generation can be increased by an average of 0.5% - 1.0%; in some projects, the power generation can be increased by more than 2%.

[0049] Blade contamination, production deviation, installation alignment deviation, etc. can cause the wind turbine to fail to operate at the optimal pitch angle before full load, thus affecting power generation performance. "Pitch angle self-optimization technology" aims at energy optimization and load safety, online analyzes the operation data of the unit to obtain the optimal pitch angle, and reduces the power generation loss caused by blade contamination and production and installation deviation. By adopting this technology, the power generation can be increased by an average of 0.5% - 1.0%; in some projects, the power generation can be increased by more than 2%.

[0050] In an optional embodiment of the present invention, in step 11, the oncoming wind information of the wind farm environment is obtained by lidar, including:

[0051] Step 111, the detection laser emitted by the lidar installed on the top of the nacelle into the atmosphere is used to obtain the reflection signal of the detection laser when it hits the suspended particles in the atmosphere;

[0052] Step 112, the reflection signal is analyzed and processed to determine the oncoming wind information.

[0053] In this embodiment, a pulsed laser beam with a specific wavelength (usually near-infrared, such as 1550 nm or 1.5 μm) is emitted into the atmosphere by the lidar installed on the top of the nacelle, the laser echo signal reflected by particles such as aerosols and dust in the air is captured by the receiver, and the oncoming wind information is obtained through Doppler frequency shift analysis, filtering and data calculation of the echo signal.

[0054] In an optional embodiment of the present invention, in step 112, the reflection signal is analyzed and processed to determine the oncoming wind information, including:

[0055] Step 1121, the reflection signal is mixed with the local oscillator light to generate an intermediate frequency signal, the frequency shift is extracted through Fourier transform to obtain the Doppler frequency shift extraction value;

[0056] Step 1122, the oncoming wind information is determined according to the Doppler frequency shift extraction value and the laser wavelength.

[0057] When the laser irradiates the moving particles (such as aerosols) in the air, the frequency of the reflected light will produce a Doppler frequency shift due to the movement speed of the particles. The frequency shift amount (Δf) is proportional to the movement speed (v) of the particles along the laser beam direction:

[0058] where λ is the laser wavelength, and θ is the angle between the laser beam and the wind direction (determined by the scanning system); by measuring Δf, the particle velocity (i.e., the component of the wind speed in the laser beam direction) can be calculated. The echo signal is mixed with the reference laser (local oscillator light) to generate an intermediate frequency signal, and the frequency shift is extracted through Fourier transform (FFT). According to the frequency shift amount Δf and the laser wavelength λ, the wind speed component (radial wind speed v r ) is calculated:

[0059]

[0060] By multi-directional scanning (at least 3 non-collinear directions) and combining geometric relationships, the horizontal wind speed (u, v) is calculated.

[0061] Planar scanning:

[0062] where φ 1 , φ 2 are the azimuth angles of two scanning directions. For example, the lidar is installed on the top of the nacelle and emits laser beams in the range of 50 - 200 m forward, scanning the horizontal sector (±15°)

[0063] Through Doppler frequency shift analysis and high-precision signal processing, the lidar provides non-contact and high-resolution oncoming wind information for the wind farm. Its implementation process covers multiple links such as laser emission, signal reception, frequency shift calculation, and three-dimensional wind speed synthesis, and requires the collaborative optimization of optical, electronic, and algorithm technologies.

[0064] In an optional embodiment of the present invention, in step 12, according to the oncoming wind information, determining the yaw correction control amount includes:

[0065] Step 121, determining the wind turbine alignment deviation according to the difference between the wind direction in the oncoming wind information and the yaw angle of the nacelle;

[0066] Here, the alignment deviation is the difference between the oncoming wind direction and the yaw angle of the nacelle:

[0067] where is the oncoming wind direction measured by the lidar, is the current yaw angle (nacelle orientation) of the wind turbine;

[0068] Step 122, obtaining the yaw correction control amount according to the wind turbine alignment deviation.

[0069] In this embodiment, the yaw correction control amount, i.e., the yaw motor speed, is dynamically adjusted according to the wind turbine alignment magnitude to prevent mechanical shock:

[0070]

[0071] Among them, ω y is the yaw motor speed, and K p is the proportional gain (such as), and K d is the differential gain (to suppress oscillation).

[0072] By calculating the wind alignment deviation based on the oncoming wind information and dynamically adjusting the yaw angle, the energy capture efficiency of the wind turbine can be significantly improved (typically increased by 3% - 8%) and the equipment life can be extended. The core lies in high-precision wind direction measurement, intelligent control algorithms, and systematic verification and optimization, ultimately achieving the upgrade of yaw control from "passive response" to "active prediction".

[0073] In an optional embodiment of the present invention, in step 13, determining the optimal gain value of the wind turbine according to the operating parameter information of the wind turbines in the wind farm includes:

[0074] Step 131, determining the blade contamination state of the wind turbine according to the real-time operating parameter information of the wind turbines in the wind farm and the air density;

[0075] Step 132, determining the optimal gain value of the wind turbine according to the blade contamination state.

[0076] In this embodiment, according to the generator power P gen of the wind turbines in the wind farm, the generator speed ω, the wind speed V, and the air density ρ, the blade contamination state of the wind turbine is determined;

[0077] Specifically, obtain the actual power coefficient;

[0078] Then, according to obtain the aerodynamic efficiency decay index; among them, η is the aerodynamic efficiency decay index, and C pt is the theoretical power coefficient; the aerodynamic efficiency decay index is used to reflect the blade contamination state of the wind turbine;

[0079] Correct the gain reference value in real time according to ρ:

[0080] ρ 0 is the designed air density, K p0 , and K i0 is the nominal gain;

[0081] According to the power loss rate: Adjust the gain:

[0082]

[0083] Among them, kdirt is the fouling compensation coefficient, which is determined by fitting historical data.

[0084] By controlling the gain self-optimization and dynamically adjusting the control parameters, the wind turbine can still track the optimal tip speed ratio under complex working conditions such as blade fouling and air density change, maximizing the power generation efficiency.

[0085] In an optional embodiment of the present invention, in step 13, according to the operation parameter information of the wind turbines in the wind farm, determining the optimal pitch angle of the wind turbine includes:

[0086] Step 133, according to the actual power coefficient operation parameter information of the wind turbines in the wind farm;

[0087] Step 134, determining the aerodynamic efficiency decay index according to the actual power coefficient operation parameter information;

[0088] Step 135, determining the optimal pitch angle of the wind turbine according to the aerodynamic efficiency decay index.

[0089] In this embodiment, according to the generator power P of the wind turbines in the wind farm gen , rotational speed ω, pitch angle β, wind speed V, and air density ρ, calculating the actual power coefficient, and then determining the aerodynamic efficiency decay index according to the actual power coefficient;

[0090] Specifically, obtaining the actual power coefficient;

[0091] Then according to obtaining the aerodynamic efficiency decay index; where η is the aerodynamic efficiency decay index, and C pt is the theoretical power coefficient;

[0092] According to β c =β o +k 1 η + k 2 Δβ i , determining the optimal pitch angle of the wind turbine;

[0093] where β c is the current pitch angle, β o is the ideal pitch angle, β o = f(V, λ o ), where is the tip speed ratio; k 1 , k 2 is the fouling compensation coefficient, and Δβ i is the installation zero deviation, and R is the gas constant.

[0094] Furthermore, the current pitch angle β cApply a small perturbation Δβ and measure the power change: Update the pitch angle along the gradient direction: β new = β old + αΔP; where α is the learning rate. β new is the optimal pitch angle, and β old is the pitch angle before adjustment, so that the wind turbine can still operate in the optimal condition when the wind speed is below the rated wind speed, increasing the power generation by 3% - 8% and prolonging the blade life at the same time.

[0095] In an alternative embodiment of the present invention, in step 14, according to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine generator set, determine the target correction strategy of the wind turbine generator set, including:

[0096] Step 141, according to the yaw correction control amount, the optimal gain value, and the optimal pitch angle of the wind turbine generator set, according to the objective function Determine the target correction strategy of the wind turbine generator set;

[0097] where γ 1 and γ 2 , M r is the blade bending moment, ω y is the yaw motor speed, and ω o is the ideal speed.

[0098] By integrating yaw correction, gain self-optimization, and pitch angle optimization, the wind turbine generator set can achieve dynamic optimal control in a complex environment, increasing the power generation efficiency by 5% - 15% and reducing the mechanical load by 10% - 20% at the same time.

[0099] In the above embodiment of the present invention, the incoming wind information of the environment where the wind farm is located and the operation parameter information of the wind turbine generator sets in the wind farm are obtained through lidar; according to the incoming wind information, the yaw correction control amount is determined; according to the operation parameter information of the wind turbine generator sets in the wind farm, the optimal gain value and the optimal pitch angle of the wind turbine generator sets are determined; according to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine generator sets, the target correction strategy of the wind turbine generator sets is determined; according to the target correction strategy, the operation state of the wind turbine generator sets is controlled. By integrating yaw correction, gain self-optimization, and pitch angle optimization, the wind turbine generator set can achieve dynamic optimal control in a complex environment.

[0100] The embodiment of the present invention also provides an intelligent correction control system 20 for a wind farm wind turbine generator set, including:

[0101] An acquisition module 21, configured to obtain the incoming wind information of the environment where the wind farm is located and the operation parameter information of the wind turbine generator sets in the wind farm through lidar;

[0102] A processing module 22, configured to determine a yaw correction control amount according to the oncoming wind information; determine an optimal gain value and an optimal pitch angle of the wind turbine according to the operating parameter information of the wind turbines in the wind farm; and determine a target correction strategy for the wind turbine according to the yaw correction control amount, the optimal gain value, and the optimal pitch angle of the wind turbine.

[0103] A control module, configured to control the operating state of the wind turbine according to the target correction strategy.

[0104] Optionally, the oncoming wind information of the environment where the wind farm is located is obtained by a lidar, including:

[0105] Detecting laser emitted into the atmosphere by a lidar installed on the top of the nacelle, and obtaining a reflection signal of the detecting laser when it hits suspended particles in the atmosphere.

[0106] Analyzing and processing the reflection signal to determine the oncoming wind information.

[0107] Optionally, analyzing and processing the reflection signal to determine the oncoming wind information includes:

[0108] Mixing the reflection signal with the local oscillator light to generate an intermediate frequency signal, and extracting the frequency shift through Fourier transform to obtain a Doppler frequency shift extraction value.

[0109] Determining the oncoming wind information according to the Doppler frequency shift extraction value and the laser wavelength.

[0110] Optionally, determining the yaw correction control amount according to the oncoming wind information includes:

[0111] Determining the wind alignment deviation of the wind turbine according to the difference between the wind direction in the oncoming wind information and the nacelle yaw angle.

[0112] Obtaining the yaw correction control amount according to the wind alignment deviation of the wind turbine.

[0113] Optionally, determining the optimal gain value of the wind turbine according to the operating parameter information of the wind turbines in the wind farm includes:

[0114] Determining the blade contamination state of the wind turbine according to the real-time operating parameter information of the wind turbines in the wind farm and the air density.

[0115] Determining the optimal gain value of the wind turbine according to the blade contamination state.

[0116] Optionally, determining the optimal pitch angle of the wind turbine according to the operating parameter information of the wind turbines in the wind farm includes:

[0117] According to the actual power coefficient operating parameter information of the wind turbines in the wind farm;

[0118] Determine the aerodynamic efficiency decay index according to the actual power coefficient operation parameter information;

[0119] Determine the optimal pitch angle of the wind turbine according to the aerodynamic efficiency decay index.

[0120] Optionally, determine the target correction strategy of the wind turbine according to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine, including:

[0121] Determine the target correction strategy of the wind turbine according to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine according to the objective function Determine the target correction strategy of the wind turbine; where P gen is the generated power, γ 1 、γ 2 ,M r is the blade bending moment, ω y is the yaw motor speed, ω o is the ideal speed.

[0122] It should be noted that this device corresponds to the above method, and all implementation manners in the above method are applicable to the embodiments of this device and can also achieve the same technical effects.

[0123] An embodiment of the present invention further provides a computing device, including: one or more processors; a storage device for storing one or more programs, when the one or more programs are executed by the one or more processors, enabling the one or more processors to implement the method as described above. All implementation manners in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0124] An embodiment of the present invention further provides a computer-readable storage medium for a computing device, characterized in that a program is stored in the computer-readable storage medium for a computing device, and when the program is executed by a processor, the method as described above is implemented. All implementation manners in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

[0125] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed in the present invention can be implemented by electronic hardware, or a combination of computing device software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraint conditions of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0126] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the systems, devices, and units described above can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein.

[0127] In the embodiments provided by the present invention, it should be understood that the disclosed devices and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces, and the indirect couplings or communication connections of the devices or units can be in electrical, mechanical, or other forms.

[0128] The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0129] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit.

[0130] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a storage medium readable by a computing device. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computing device software product is stored in a storage medium and includes several instructions to enable a computing device (which can be a personal computing device, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, ROM, RAM, magnetic disks, or optical discs that can store program codes.

[0131] In addition, it should be noted that in the device and method of the present invention, obviously, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations shall be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above series of processes can naturally be executed in chronological order according to the described order, but it is not necessary to be executed in chronological order. Some steps can be executed in parallel or independently of each other. For those of ordinary skill in the art, it is understandable that all or any steps or components of the method and device of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in the form of hardware, firmware, software, or a combination thereof, which can be achieved by those of ordinary skill in the art using their basic programming skills after reading the description of the present invention.

[0132] Therefore, the object of the present invention can also be achieved by running a program or a set of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the object of the present invention can also be achieved only by providing a program product containing program code for implementing the method or device. That is to say, such a program product also constitutes the present invention, and a storage medium storing such a program product also constitutes the present invention. Obviously, the storage medium can be any well-known storage medium or any storage medium developed in the future. It should also be noted that in the device and method of the present invention, obviously, each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations shall be regarded as equivalent solutions of the present invention. Moreover, the steps of performing the above series of processes can naturally be executed in chronological order according to the described order, but it is not necessary to be executed in chronological order. Some steps can be executed in parallel or independently of each other.

[0133] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent correction control method for a wind turbine in a wind farm, characterized in that: include: Obtain wind flow information of the wind farm environment and operating parameter information of wind turbines in the wind farm through LiDAR; Determining a yaw correction control amount according to the incoming wind information; Determining an optimal gain value and an optimal pitch angle of a wind turbine generator set according to operating parameter information of the wind turbine generator set of the wind farm; Determining a target correction strategy for the wind turbine generator set according to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine generator set; According to the target correction strategy, the operating state of the wind turbine generator set is controlled.

2. The intelligent correction control method for wind turbines in a wind farm according to claim 1 is characterized in that: The laser radar is used to obtain the incoming wind information of the wind farm environment, including: The laser radar installed on the top of the cabin emits a detection laser into the atmosphere to obtain the reflection signal of the detection laser when it hits the suspended particles in the atmosphere; The reflected signal is analyzed and processed to determine the incoming wind information.

3. The intelligent correction control method for wind turbines in a wind farm according to claim 1 is characterized in that: Analyzing and processing the reflected signal to determine the incoming wind information includes: The reflected signal is mixed with the local oscillator light to generate an intermediate frequency signal, and the frequency shift is extracted by Fourier transform to obtain a Doppler frequency shift extraction value; The incoming wind information is determined based on the Doppler frequency shift extraction value and the laser wavelength.

4. The intelligent correction control method for wind turbines in a wind farm according to claim 1, characterized in that: Determining a yaw correction control amount according to the incoming wind information includes: Determining the wind deviation of the wind turbine according to the difference between the wind direction in the incoming wind information and the yaw angle of the nacelle; According to the wind turbine deviation, the yaw correction control quantity is obtained.

5. The intelligent correction control method for wind turbines in a wind farm according to claim 1, characterized in that: Determining the optimal gain value of the wind turbine generator set according to the operating parameter information of the wind turbine generator set of the wind farm includes: Determining the blade contamination status of the wind turbine according to the real-time operating parameter information of the wind turbine of the wind farm and the air density; An optimal gain value of the wind turbine generator system is determined according to the blade contamination state.

6. The intelligent correction control method for wind turbines in a wind farm according to claim 1, characterized in that: Determining the optimal pitch angle of the wind turbine generator set according to the operating parameter information of the wind turbine generator set in the wind farm includes: According to the actual power coefficient operation parameter information of the wind turbine generator set of the wind farm; Determine the aerodynamic efficiency attenuation index based on the actual power coefficient operating parameter information; According to the aerodynamic efficiency attenuation index, the optimal pitch angle of the wind turbine is determined.

7. The intelligent correction control method for wind turbines in a wind farm according to claim 1, characterized in that: According to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine generator set, a target correction strategy of the wind turbine generator set is determined, including: According to the yaw correction control amount, the optimal gain value of the wind turbine, and the optimal pitch angle, according to the objective function Determine the target correction strategy for wind turbines; where P gen is the power generation, γ1, γ2, M r is the blade bending moment, ω y is the yaw motor speed, ω o The ideal speed.

8. An intelligent correction control system for a wind turbine in a wind farm, characterized in that: include: An acquisition module is used to acquire wind flow information of the environment where the wind farm is located and operating parameter information of the wind turbines in the wind farm through a laser radar; A processing module, configured to determine a yaw correction control amount according to the incoming wind information; determine an optimal gain value and an optimal pitch angle of the wind turbine according to the operating parameter information of the wind turbine of the wind farm; and determine a target correction strategy of the wind turbine according to the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine; A control module is used to control the operating state of the wind turbine generator set according to the target correction strategy.

9. A computing device, characterized in that include: one or more processors; A storage device, used for storing one or more programs, when the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 7.

10. A computing device readable storage medium, characterized in that: The computing device readable storage medium stores a program, which, when executed by a processor, implements the method according to any one of claims 1 to 7.

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