An intelligent correction control method and system for wind turbines in a wind farm

Abstract

The present application provides an intelligent correction control method and system for wind turbines in a wind farm, the method comprising: obtaining wind flow information of the wind farm environment and operating parameter information of the wind turbines in the wind farm through a laser radar; determining a yaw correction control amount based on the wind flow information; determining an optimal gain value and an optimal pitch angle of the wind turbine based on the operating parameter information of the wind turbines in the wind farm; determining a target correction strategy for the wind turbine based on the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine; and controlling the operating state of the wind turbine based on the target correction strategy. The solution of the present application integrates yaw correction, gain self-optimization, and pitch angle optimization, so that the wind turbine can achieve dynamic optimal control in complex environments.

Description

Technical Field

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

[0002] Wind power generation is a renewable and clean energy source. It is an important form of wind energy utilization, a renewable, pollution-free, high-energy, and promising energy source. Consequently, wind farm control technology has emerged, playing a crucial role in improving the performance and stability of wind power generation systems and increasing wind energy efficiency.

[0003] In the prior art, the wind measurement accuracy of the wind vane is affected by the impeller. Calibration deviations and unreasonable yaw movements will cause the wind turbine to have wind deviations, thereby affecting the wind turbine's power generation performance. Summary of the Invention

[0004] The present 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 turbines can achieve dynamic optimal control in complex environments.

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

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

[0007] Obtain wind flow information of the wind farm environment and operating parameter information of the wind turbines in the wind farm through lidar;

[0008] determining a yaw correction control amount according to the incoming wind information;

[0009] determining an optimal gain value and an optimal pitch angle of the wind turbine generator set according to operating parameter information of the wind turbine generator set of the wind farm;

[0010] determining a target correction strategy for the wind turbine generator set based on the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine generator set;

[0011] According to the target correction strategy, the operating state of the wind turbine generator set is controlled.

[0012] Optionally, obtain wind flow information about the wind farm environment using LiDAR, including:

[0013] The laser radar installed on the top of the cabin emits a detection laser into the atmosphere and obtains the reflection signal of the detection laser when it hits the suspended particles in the atmosphere;

[0014] The reflected signal is analyzed and processed to determine incoming wind information.

[0015] Optionally, analyzing and processing the reflected signal to determine incoming wind information includes:

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

[0017] The incoming wind information is determined based on the Doppler frequency shift extraction value and the laser wavelength.

[0018] Optionally, determining a yaw correction control amount according to the incoming wind information includes:

[0019] Determining the wind deviation of the wind turbine generator system according to the difference between the wind direction in the incoming wind information and the yaw angle of the nacelle;

[0020] According to the wind turbine's deviation from the wind, the yaw correction control value is obtained.

[0021] Optionally, determining the optimal gain value of the wind turbine generator set according to the operating parameter information of the wind turbine generator set in the wind farm includes:

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

[0023] An optimal gain value of the wind turbine generator system is determined according to the blade contamination state.

[0024] Optionally, 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:

[0025] According to the actual power coefficient operating parameter information of the wind turbine generator set of the wind farm;

[0026] Determine the aerodynamic efficiency attenuation index based on the actual power coefficient operating parameter information;

[0027] According to the aerodynamic efficiency attenuation index, the optimal pitch angle of the wind turbine is determined.

[0028] Optionally, 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 includes:

[0029] 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 of the wind turbine; 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 For the ideal speed.

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

[0031] An acquisition module is used to obtain 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;

[0032] a processing module, configured to determine a yaw correction control amount based on the incoming wind information; determine an optimal gain value and an optimal pitch angle of the wind turbine according to operating parameter information of the wind turbine of 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;

[0033] A control module is used to control the operating state of the wind turbine generator set according to the target correction strategy.

[0034] An embodiment of the present invention also provides a computing device, comprising: one or more processors; a storage device for storing one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described above.

[0035] An embodiment of the present invention further provides a computing device readable storage medium, wherein the computing device readable storage medium stores a program, and when the program is executed by a processor, the method described above is implemented.

[0036] The above technical solutions of this application have at least the following technical effects:

[0037] The above-mentioned solution of the present application uses a laser radar to obtain information about the incoming wind flow in the wind farm environment and the operating parameter information of the wind turbines in the wind farm; determines a yaw correction control variable based on the incoming wind flow information; determines the optimal gain value and optimal pitch angle of the wind turbine based on the operating parameter information of the wind turbines in the wind farm; determines a target correction strategy for the wind turbine based on the yaw correction control variable, the optimal gain value and optimal pitch angle of the wind turbine; and controls the operating state of the wind turbine based on the target correction strategy. By integrating yaw correction, gain self-optimization, and pitch angle optimization, the wind turbine can achieve dynamic optimal control in complex environments. BRIEF DESCRIPTION OF THE DRAWINGS

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

[0039] Figure 2 A schematic diagram of the modules of the intelligent correction control system for wind turbines in a wind farm provided in an embodiment of the present application. DETAILED DESCRIPTION

[0040] To make the purpose, technical solutions, and advantages of the embodiments of this application more clear, the technical solutions in the embodiments of this application are clearly and completely described below. Obviously, the described embodiments are part of the embodiments of this application, not all of them. Based on the embodiments in this application, all other embodiments obtained by ordinary technicians in this field without making creative efforts also fall within the scope of protection of this application.

[0041] like Figure 1 As shown, an embodiment of the present application provides an intelligent correction control method for a wind turbine generator set in a wind farm, comprising:

[0042] Step 11: Use LiDAR to obtain wind information about the wind farm's surroundings and operating parameters of the wind turbines. This includes information about blade contamination and pitch angles. Furthermore, this information may include information collected by generator sensors, such as power output, pitch angle, yaw angle, generator speed, torque (inverter data), vibration, and temperature (condition monitoring system).

[0043] Step 12: determining a yaw correction control amount based on the incoming wind information;

[0044] Step 13, determining an optimal gain value and an 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;

[0045] Step 14, 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;

[0046] Step 15: Control the operating state of the wind turbine generator system according to the target correction strategy.

[0047] This embodiment of the present application, by integrating yaw correction, gain self-optimization and pitch angle optimization, the wind turbine can achieve dynamic optimal control in a complex environment. Specifically, a laser of a specific wavelength is emitted by a laser radar, and the reflected signal of the laser when it encounters "particles" suspended in the atmosphere (including liquid and solid tiny particles, such as water vapor, pollen, dust, etc.) is actively detected. By analyzing the Doppler frequency shift of the laser and then through signal processing and other algorithm analysis, accurate and reliable incoming wind information can be obtained. The wind speed and direction measured by the laser radar are not affected by the disturbance of the impeller. The wind deviation of the unit is obtained through the wind deviation optimization module, and precise wind is achieved through intelligent yaw control, which reduces the power generation loss caused by wind deviation and increases the annual power generation by 0.3% to 2%.

[0048] Blade contamination and air density fluctuations can cause wind turbines to lose their optimal tip speed ratio, impacting power generation performance. "Control Gain Self-Optimization" aims to optimize energy efficiency by analyzing turbine operating data online to determine the optimal gain value. Dynamic adjustments are made in real time to account for air density changes, ensuring optimal operation despite varying blade surface contamination and air density. This technology can increase power generation by an average of 0.5% to 1.0%, with some projects seeing increases of over 2%.

[0049] Blade contamination, production deviations, and installation misalignment can cause wind turbines to fail to operate at the optimal pitch angle before reaching full power, impacting power generation performance. "Automated Pitch Angle Optimization Technology" aims to optimize energy and load safety by analyzing turbine operating data online to determine the optimal pitch angle, minimizing power generation losses caused by blade contamination and production and installation deviations. This technology can increase power generation by an average of 0.5% to 1.0%, with some projects achieving increases of over 2%.

[0050] In an optional embodiment of the present invention, in step 11, obtaining incoming wind information of the environment in which the wind farm is located by using a laser radar includes:

[0051] Step 111: emitting a detection laser into the atmosphere through a laser radar installed on top of the cabin, and obtaining a reflection signal of the detection laser hitting suspended particles in the atmosphere;

[0052] Step 112: Analyze and process the reflected signal to determine incoming wind information.

[0053] In this embodiment, a laser radar installed on the top of the cabin emits a pulsed laser beam of a specific wavelength (usually near-infrared, such as 1550nm or 1.5μm) into the atmosphere, and a receiver captures the laser echo signal reflected by aerosols, dust and other particles in the air. The echo signal is subjected to Doppler frequency shift analysis, filtering and data solution to obtain the incoming wind information.

[0054] In an optional embodiment of the present invention, in step 112, analyzing and processing the reflected signal to determine the incoming wind information includes:

[0055] Step 1121: Mix the reflected signal with the local oscillator light to generate an intermediate frequency signal, extract the frequency shift through Fourier transform, and obtain a Doppler frequency shift extraction value;

[0056] Step 1122: Determine incoming wind information based on the Doppler frequency shift extraction value and the laser wavelength.

[0057] When laser light strikes moving particles in the air (such as aerosols), the frequency of the reflected light will undergo a Doppler shift due to the particle's velocity. The amount of frequency shift (Δf) is proportional to the particle's velocity (v) along the laser beam:

[0058] Where λ is the laser wavelength, 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 direction of the laser beam) can be solved. The echo signal is mixed with the reference laser (local oscillator light) to generate an intermediate frequency signal. The frequency shift is extracted by Fourier transform (FFT). Based on the frequency shift Δf and the laser wavelength λ, the wind speed component in the direction of the laser beam (radial wind speed v r ):

[0059]

[0060] The horizontal wind speed (u, v) is calculated by multi-directional scanning (at least 3 non-collinear directions) combined with geometric relationships.

[0061] Plane scan:

[0062] Among them, φ1 and φ2 are the azimuth angles of the two scanning directions. For example, the laser radar is installed on the top of the cabin, emitting a laser beam in the range of 50 to 200 meters forward, scanning the horizontal sector (±15°)

[0063] LiDAR provides wind farms with non-contact, high-resolution incoming wind information through Doppler shift analysis and high-precision signal processing. Its implementation involves multiple steps, including laser emission, signal reception, frequency shift calculation, and 3D wind speed synthesis, requiring the coordinated optimization of optical, electronic, and algorithmic technologies.

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

[0065] Step 121, 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;

[0066] Here, the wind deviation is the difference between the incoming wind direction and the yaw angle of the cabin:

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

[0068] Step 122: Obtain a yaw correction control value according to the wind turbine generator set's deviation from the wind.

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

[0070]

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

[0072] By calculating wind deviation based on incoming wind information and dynamically adjusting the yaw angle, wind turbine energy capture efficiency can be significantly improved (typically by 3% to 8%) and equipment lifespan can be extended. The core of this approach lies in high-precision wind direction measurement, intelligent control algorithms, and systematic verification and optimization, ultimately achieving an upgrade in 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 generator set according to the operating parameter information of the wind turbine generator set in the wind farm includes:

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

[0075] Step 132: determining an optimal gain value of the wind turbine generator system according to the blade contamination status.

[0076] In this embodiment, the power generated by the wind turbine generator of the wind farm is P gen , generator speed ω, wind speed V, air density ρ, to determine the blade contamination status of the wind turbine;

[0077] Specifically, Get the actual power factor;

[0078] Based on The aerodynamic efficiency attenuation index is obtained; where η is the aerodynamic efficiency attenuation index, C pt is the theoretical power coefficient; the aerodynamic efficiency attenuation 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] ρ0 is the design air density, K p0 , K i0 is the nominal gain;

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

[0082]

[0083] Among them, k dirtis the contamination compensation coefficient, which is determined by fitting historical data.

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

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

[0086] Step 133, operating parameter information based on actual power coefficient of the wind turbine generator set in the wind farm;

[0087] Step 134 , determining an aerodynamic efficiency attenuation index based on the actual power coefficient operating parameter information;

[0088] Step 135 : determining the optimal pitch angle of the wind turbine generator system according to the aerodynamic efficiency attenuation index.

[0089] In this embodiment, according to the generator power P of the wind turbine generator set of the wind farm, gen , rotation speed ω, pitch angle β, wind speed V, air density ρ, determine the actual power coefficient, and then determine the aerodynamic efficiency attenuation index based on the actual power coefficient;

[0090] Specifically, Get the actual power factor;

[0091] Based on The aerodynamic efficiency attenuation index is obtained; where η is the aerodynamic efficiency attenuation index, C pt is the theoretical power coefficient;

[0092] According to β c =β o +k1η+k2Δβ i , determine the optimal pitch angle of the wind turbine;

[0093] Among them, β c is the current pitch angle, β o is the ideal pitch angle, β o =f(V,λ o ),in, is the tip speed ratio; k1, k2 are the pollution compensation coefficients, Δβ i is the installation zero deviation, and R is the gas constant.

[0094] Furthermore, the current pitch angle β c Apply a small disturbance Δβ to the basis 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, β old The pitch angle before adjustment allows the wind turbine to operate at the optimal working condition when the wind speed is lower than the rated wind speed, increasing power generation by 3% to 8% and extending blade life.

[0095] In an optional embodiment of the present invention, in step 14, determining a target correction strategy for the wind turbine generator set based on the yaw correction control variable, the optimal gain value, and the optimal pitch angle of the wind turbine generator set includes:

[0096] Step 141, 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;

[0097] Among them, γ1, γ2, M r is the blade bending moment, ω y is the yaw motor speed, ω o For the ideal speed.

[0098] By integrating yaw correction, gain self-optimization and pitch angle optimization, wind turbines can achieve dynamic optimal control in complex environments, improving power generation efficiency by 5% to 15% while reducing mechanical loads by 10% to 20%.

[0099] In one embodiment of the present invention, a laser radar is used to obtain wind flow information from the wind farm environment and operating parameter information of the wind turbines in the wind farm; a yaw correction control variable is determined based on the wind flow information; an optimal gain value and an optimal pitch angle of the wind turbine are determined based on the operating parameter information of the wind turbines in the wind farm; a target correction strategy for the wind turbine is determined based on the yaw correction control variable, the optimal gain value, and the optimal pitch angle of the wind turbine; and the operating state of the wind turbine is controlled based on the target correction strategy. By integrating yaw correction, gain self-optimization, and pitch angle optimization, the wind turbine can achieve dynamic optimal control in complex environments.

[0100] An embodiment of the present invention further provides an intelligent correction control system 20 for a wind turbine generator set in a wind farm, comprising:

[0101] An acquisition module 21 is configured to acquire wind flow information of the environment in which the wind farm is located and operating parameter information of the wind turbines in the wind farm through a laser radar;

[0102] The processing module 22 is configured to determine a yaw correction control amount based on the incoming wind information; determine an optimal gain value and an optimal pitch angle of the wind turbine based on the operating parameter information of the wind turbine in the wind farm; and determine a target correction strategy for the wind turbine based on the yaw correction control amount, the optimal gain value, and the optimal pitch angle of the wind turbine;

[0103] A control module is used to control the operating state of the wind turbine generator set according to the target correction strategy.

[0104] Optionally, obtain wind flow information about the wind farm environment using LiDAR, including:

[0105] The laser radar installed on the top of the cabin emits a detection laser into the atmosphere and obtains the reflection signal of the detection laser when it hits the suspended particles in the atmosphere;

[0106] The reflected signal is analyzed and processed to determine incoming wind information.

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

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

[0109] The incoming wind information is determined based on the Doppler frequency shift extraction value and the laser wavelength.

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

[0111] Determining the wind deviation of the wind turbine generator system according to the difference between the wind direction in the incoming wind information and the yaw angle of the nacelle;

[0112] According to the wind turbine's deviation from the wind, the yaw correction control value is obtained.

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

[0114] determining a blade contamination state of the wind turbine according to real-time operating parameter information of the wind turbine in the wind farm and air density;

[0115] An optimal gain value of the wind turbine generator system is determined according to the blade contamination state.

[0116] Optionally, 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:

[0117] According to the actual power coefficient operating parameter information of the wind turbine generator set of the wind farm;

[0118] Determine the aerodynamic efficiency attenuation index based on the actual power coefficient operating parameter information;

[0119] According to the aerodynamic efficiency attenuation index, the optimal pitch angle of the wind turbine is determined.

[0120] Optionally, 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 includes:

[0121] 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 of the wind turbine; 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 For the ideal speed.

[0122] It should be noted that the device is a device corresponding to the above method, and all implementation methods of the above method are applicable to the embodiments of the device and can achieve the same technical effects.

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

[0124] An embodiment of the present invention further provides a computing device-readable storage medium, characterized in that the computing device-readable storage medium stores a program that, when executed by a processor, implements the method described above. All implementations in the above method embodiments are applicable to this embodiment and can achieve the same technical effects.

[0125] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed in the present invention can be implemented in electronic hardware, or a combination of computing device software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0126] Those skilled in the art will 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 aforementioned method embodiments and will not be repeated here.

[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 merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the mutual coupling or direct coupling or communication connection shown or discussed can be through some interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

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

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

[0130] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a storage medium readable by a computing device. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computing device software product is stored in a storage medium and includes a number of instructions for enabling a computing device (which can be a personal computing device, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes various media that can store program code, such as a USB flash drive, a mobile hard disk, ROM, RAM, a magnetic disk, or an optical disk.

[0131] In addition, it should be noted that, in the apparatus and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. Moreover, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but it is not necessary to perform them in chronological order, and some steps can be performed in parallel or independently of each other. For those of ordinary skill in the art, it will be understood that all or any steps or components of the method and apparatus of the present invention can be implemented in any computing device (including processors, storage media, etc.) or a network of computing devices in 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 purpose of the present invention can also be achieved by running a program or a group of programs on any computing device. The computing device can be a well-known general-purpose device. Therefore, the purpose of the present invention can also be achieved simply by providing a program product containing program code that implements the method or device. That is to say, such a program product also constitutes the present invention, and the 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 pointed out that in the device and method of the present invention, it is obvious that each component or each step can be decomposed and / or recombined. These decompositions and / or recombinations should be regarded as equivalent schemes of the present invention. In addition, the steps of performing the above-mentioned series of processing can naturally be performed in chronological order according to the order of description, but do not necessarily need to be performed in chronological order. Certain steps can be performed 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 above embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the above embodiments, or make equivalent replacements for some or all of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. An intelligent correction control method for wind turbines in a wind farm, characterized in that: include: Obtain wind flow information of the wind farm environment and operating parameter information of the 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 the 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 based on the yaw correction control amount, the optimal gain value and the optimal pitch angle of the wind turbine generator set; controlling the operating state of the wind turbine generator system according to the target correction strategy; The step of determining the optimal gain value of the wind turbine generator set according to the operating parameter information of the wind turbine generator set in the wind farm includes: determining a blade contamination state of the wind turbine according to real-time operating parameter information of the wind turbine in the wind farm and air density; determining an optimal gain value of the wind turbine generator set according to the blade contamination state; Wherein, determining a target correction strategy of 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 includes: 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 of the wind turbine; among them, is the power generation power, 、 , is the blade bending moment, is the yaw motor speed, For the ideal speed.

2. The intelligent correction control method for wind turbines in a wind farm according to claim 1, characterized in that: The laser radar is used to obtain wind flow information in the wind farm environment, including: The laser radar installed on the top of the cabin emits a detection laser into the atmosphere and obtains 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 incoming wind information.

3. The intelligent correction control method for wind turbines in a wind farm according to claim 2, characterized in that: Analyzing and processing the reflected signal to determine incoming wind information includes: Mixing the reflected signal with the local oscillator light to generate an intermediate frequency signal, extracting the frequency shift through 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 based on the incoming wind information includes: Determining the wind deviation of the wind turbine generator system 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's deviation from the wind, the yaw correction control value 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 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: determining an actual power coefficient according to operating parameter information of the wind turbine generator set of the wind farm; Determine the aerodynamic efficiency attenuation index based on the actual power coefficient; According to the aerodynamic efficiency attenuation index, the optimal pitch angle of the wind turbine is determined.

6. An intelligent correction control system for wind turbines in a wind farm, characterized in that: include: An acquisition module is used to obtain 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 based on the incoming wind information; determine an optimal gain value and an optimal pitch angle of the wind turbine according to operating parameter information of the wind turbine of 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; A control module, configured to control the operating state of the wind turbine generator system according to the target correction strategy; The step of determining the optimal gain value of the wind turbine generator set according to the operating parameter information of the wind turbine generator set in the wind farm includes: determining a blade contamination state of the wind turbine according to real-time operating parameter information of the wind turbine in the wind farm and air density; determining an optimal gain value of the wind turbine generator set according to the blade contamination state; Wherein, determining a target correction strategy of 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 includes: 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 of the wind turbine; among them, is the power generation power, 、 , is the blade bending moment, is the yaw motor speed, For the ideal speed.

7. A computing device, characterized in that include: one or more processors; A storage device for storing one or more programs, wherein 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 5.

8. A computing device readable storage medium, characterized in that: The computing device readable storage medium stores a program, which implements the method according to any one of claims 1 to 5 when executed by a processor.

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