Wind power generator control method and system based on dynamic yaw optimization

Abstract

This application discloses a wind turbine control method and system based on dynamic yaw optimization, relating to the field of wind turbine control technology. The method includes: measuring wind speed and direction at a forward measurement point in front of the wind turbine using a lidar mounted on the wind turbine nacelle to obtain forward wind information; acquiring the wind turbine's current yaw angle, current wind speed, generator power output, and nacelle acceleration in real time; predicting the target yaw angle of the wind turbine within a prediction time window based on the forward wind information, current yaw angle, current wind speed, generator power output, and nacelle acceleration; generating a yaw control command based on the target yaw angle; and sending the yaw control command to the yaw drive system to enable the wind turbine to control the wind ahead of time. This method solves the technical problems of low wind energy capture efficiency and high turbine load caused by yaw control response lag in existing technologies.

Description

Technical Field

[0001] This application relates to the field of wind turbine generator control technology, and in particular to a wind turbine generator control method and system based on dynamic yaw optimization. Background Technology

[0002] With the increasing global demand for renewable energy, wind power, as a crucial component of clean energy, has garnered significant attention for its power generation efficiency and operational stability. The yaw control system is a key subsystem of wind turbines, primarily responsible for adjusting the nacelle orientation to ensure the rotor faces the incoming wind direction, maximizing wind energy capture. Traditional yaw control relies mainly on measuring the instantaneous wind direction using a weathervane mounted at the nacelle and making lag adjustments accordingly. However, due to the dynamic and uncertain nature of wind changes, this responsive control method suffers from significant delays, leading to reduced wind energy capture efficiency. Furthermore, frequent yaw maneuvers can exacerbate the mechanical load on critical components (such as blades, tower, and bearings), impacting turbine lifespan.

[0003] To overcome the limitations of traditional methods, forward-looking measurement techniques have been introduced in recent years to predict wind speed and direction, enabling proactive wind control. While existing technologies can adjust yaw angles based on predicted wind speed information, they primarily focus on short-term wind direction tracking and do not fully consider the dynamic coupling relationship between wind turbine operating conditions and forward-looking wind information. Furthermore, the lack of a multi-objective optimization mechanism that comprehensively considers power generation efficiency and structural loads within the prediction time window makes it difficult for yaw control to effectively reduce fatigue damage while simultaneously increasing power generation.

[0004] The above content is only used to help understand the technical solution of the present invention and does not represent an admission that the above content is prior art. Summary of the Invention

[0005] The main purpose of this application is to provide a wind turbine control method and system based on dynamic yaw optimization, which aims to solve the technical problems of low wind energy capture efficiency and high unit load caused by yaw control response lag in the prior art.

[0006] To achieve the above objectives, this application provides a wind turbine control method based on dynamic yaw optimization, the method comprising:

[0007] By using lidar installed on the nacelle of a wind turbine, wind speed and direction are measured at forward measurement points in front of the turbine to obtain forward wind information.

[0008] The current yaw angle, current wind speed, generator power output, and nacelle acceleration of the wind turbine are acquired in real time.

[0009] Based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration, the target yaw angle of the wind turbine within the prediction time window is predicted.

[0010] Based on the target yaw angle, a yaw control command is generated and sent to the yaw drive system to enable the wind turbine to control the wind ahead of the wind.

[0011] In one embodiment, the step of predicting the target yaw angle of the wind turbine within the prediction time window based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration includes:

[0012] The forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration are time-series aligned, and the aligned data is used as the input vector.

[0013] Within the prediction time window, based on the current unit status and the input vector, the predicted power generation and key component load of the wind turbine are predicted.

[0014] The target yaw angle is determined based on the predicted power generation and the load on the key components.

[0015] In one embodiment, the step of aligning the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration in a time series, and using the aligned data as an input vector, includes:

[0016] The timestamps of the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration are determined respectively, and the timestamps are mapped to the standard time base respectively to obtain the aligned timestamps;

[0017] Arrange the data based on the alignment timestamps, determine the missing data positions, and determine the preceding and following data positions of the missing data positions;

[0018] The data filling value is determined based on the preceding data and the following data;

[0019] The data filler values ​​are used to fill the missing data positions to obtain aligned data;

[0020] An input vector is generated based on the alignment data.

[0021] In one embodiment, the step of predicting the predicted power generation and critical component load of the wind turbine based on the current unit status and the input vector within a prediction time window includes:

[0022] Within the forecast time window, the adjustment step size of the yaw angle is determined based on the current crew status;

[0023] Based on the adjusted step size, a rolling simulation is performed on the input vector to obtain the simulation results;

[0024] Based on the simulation results, the predicted power generation and key component load sequence corresponding to each time step and each candidate yaw angle within the prediction time window are determined.

[0025] In one embodiment, the step of determining the target yaw angle based on the predicted power generation and the load on the critical component includes:

[0026] A comprehensive evaluation function is constructed by taking the cumulative predicted power generation within the predicted time window as the positive term and the weighted cumulative value of the load of key components as the negative term.

[0027] Traverse the candidate yaw angle strategies and determine the comprehensive evaluation function value corresponding to the candidate yaw angle strategy based on the comprehensive evaluation function;

[0028] The candidate yaw angle strategy corresponding to the largest comprehensive evaluation function value among the comprehensive evaluation function values ​​is determined as the target yaw angle strategy, and the target yaw angle is determined according to the target yaw angle strategy.

[0029] In one embodiment, the step of generating yaw control commands based on the target yaw angle includes:

[0030] Calculate the angular deviation between the target yaw angle and the current actual yaw angle of the wind turbine;

[0031] Based on the angle deviation, preset yaw rate, and preset acceleration limit, a yaw motor control signal is generated;

[0032] The yaw motor control signal is encapsulated using a standard communication protocol to obtain the yaw control command.

[0033] In one embodiment, the step of acquiring the current yaw angle, current wind speed, generator power output, and nacelle acceleration of the wind turbine in real time includes:

[0034] The original yaw angle, original wind speed, original generator power output, and original nacelle acceleration of the wind turbine are collected in real time.

[0035] The original yaw angle, original wind speed, original generator power output, and original nacelle acceleration are corrected to obtain corrected data.

[0036] The corrected data is subjected to a consistency check. If the consistency check is successful, the corrected data is determined as the current yaw angle, current wind speed, generator power output, and nacelle acceleration.

[0037] In one embodiment, the step of measuring wind speed and direction at a forward measurement point in front of the wind turbine using a lidar installed on the wind turbine nacelle to obtain forward wind information includes:

[0038] Based on the rotor diameter and rated wind speed of the wind turbine, determine the target look-ahead measurement points;

[0039] The lidar installed on the wind turbine nacelle is used to continuously scan the target forward measurement point, collect multiple sets of wind speed and wind direction data, and generate wind information.

[0040] The wind information is preprocessed to obtain forward wind information.

[0041] In one embodiment, the step of determining the target look-ahead measurement point based on the rotor diameter and rated wind speed of the wind turbine includes:

[0042] The initial look-ahead measurement point is determined based on the rotor diameter of the wind turbine.

[0043] Wind shear information is determined based on the geographical environment information and turbulence information of the location of the wind turbine.

[0044] The initial look-ahead measurement points are optimized based on the rated wind speed of the wind turbine and the wind shear information, and the optimized look-ahead measurement points are determined as the target look-ahead measurement points.

[0045] Furthermore, to achieve the above objectives, this application also proposes a wind turbine control system based on dynamic yaw optimization, which includes:

[0046] The wind sensing module is used to measure wind speed and direction at forward measurement points in front of the wind turbine using a lidar installed on the nacelle of the wind turbine, thereby obtaining forward wind information.

[0047] The generator sensing module is used to acquire the current yaw angle, current wind speed, generator power output and nacelle acceleration of the wind turbine in real time.

[0048] The yaw prediction module is used to predict the target yaw angle of the wind turbine within the prediction time window based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration.

[0049] The yaw control module is used to generate yaw control commands based on the target yaw angle and send the yaw control commands to the yaw drive system so as to enable the wind turbine to control the wind ahead of the wind.

[0050] In addition, to achieve the above objectives, this application also proposes a wind turbine control device based on dynamic yaw optimization. The wind turbine control device based on dynamic yaw optimization includes: a memory, a processor, and a computer program stored in the memory and executable on the processor. The computer program is configured to implement the steps of the wind turbine control method based on dynamic yaw optimization as described above.

[0051] In addition, to achieve the above objectives, the present invention also proposes a storage medium, which is a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, it implements the steps of the wind turbine control method based on dynamic yaw optimization as described above.

[0052] In addition, to achieve the above objectives, this application also provides a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the wind turbine control method based on dynamic yaw optimization as described above.

[0053] This application provides a wind turbine control method based on dynamic yaw optimization. A lidar mounted on the wind turbine nacelle measures wind speed and direction at a forward measurement point in front of the turbine to obtain forward wind information. The method also acquires the wind turbine's current yaw angle, current wind speed, generator power output, and nacelle acceleration in real time. Based on this forward wind information, current yaw angle, current wind speed, generator power output, and nacelle acceleration, the method predicts the target yaw angle of the wind turbine within the prediction time window. Based on the target yaw angle, a yaw control command is generated and sent to the yaw drive system to enable the wind turbine to proactively control the wind. This method solves the technical problems of low wind energy capture efficiency and high unit load caused by yaw control response lag in existing technologies. Attached Figure Description

[0054] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0055] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0056] Figure 1 This is a flowchart illustrating an embodiment of the wind turbine control method based on dynamic yaw optimization in this application.

[0057] Figure 2 This is a schematic diagram of wind turbine yaw in an embodiment of the wind turbine control method based on dynamic yaw optimization according to this application.

[0058] Figure 3 This is a schematic diagram of the module structure of the wind turbine control system based on dynamic yaw optimization in an embodiment of this application;

[0059] Figure 4 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the wind turbine control method based on dynamic yaw optimization in the embodiments of this application.

[0060] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0061] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0062] To better understand the technical solution of this application, a detailed description will be provided below in conjunction with the accompanying drawings and specific implementation methods.

[0063] The main solution of this application embodiment is: to measure the wind speed and direction at the forward measurement point in front of the wind turbine by using a lidar installed on the wind turbine nacelle, and to obtain forward wind information;

[0064] The current yaw angle, current wind speed, generator power output, and nacelle acceleration of the wind turbine are acquired in real time.

[0065] Based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration, the target yaw angle of the wind turbine within the prediction time window is predicted.

[0066] Based on the target yaw angle, a yaw control command is generated and sent to the yaw drive system to enable the wind turbine to control the wind ahead of the wind.

[0067] Currently, with the increasing global demand for renewable energy, wind power, as an important component of clean energy, has received widespread attention for its power generation efficiency and operational stability. The yaw control system is one of the key subsystems of a wind turbine, primarily responsible for adjusting the nacelle orientation to ensure the rotor faces the incoming wind direction, maximizing wind energy capture. Traditional yaw control mainly relies on measuring the instantaneous wind direction using a weathervane installed at the nacelle and making lag adjustments accordingly. However, due to the dynamic and uncertain nature of wind changes, this responsive control method suffers from significant delays, leading to reduced wind energy capture efficiency. Furthermore, frequent yaw movements can exacerbate the mechanical load on critical components (such as blades, towers, and bearings), affecting the turbine's lifespan.

[0068] To overcome the limitations of traditional methods, forward-looking measurement techniques have been introduced in recent years to predict wind speed and direction, enabling proactive wind control. While existing technologies can adjust yaw angles based on predicted wind speed information, they primarily focus on short-term wind direction tracking and do not fully consider the dynamic coupling relationship between wind turbine operating conditions and forward-looking wind information. Furthermore, the lack of a multi-objective optimization mechanism that comprehensively considers power generation efficiency and structural loads within the prediction time window makes it difficult for yaw control to effectively reduce fatigue damage while simultaneously increasing power generation.

[0069] This application provides a solution that uses a lidar mounted on the nacelle of a wind turbine to measure wind speed and direction at a forward measurement point in front of the turbine, acquiring forward wind information. It also acquires the wind turbine's current yaw angle, current wind speed, generator power output, and nacelle acceleration in real time. Based on this forward wind information, current yaw angle, current wind speed, generator power output, and nacelle acceleration, it predicts the wind turbine's target yaw angle within a prediction time window. Based on the target yaw angle, it generates yaw control commands and sends them to the yaw drive system, enabling the wind turbine to proactively control the wind. This approach solves the technical problems of low wind energy capture efficiency and high turbine load caused by lag in yaw control response in existing technologies.

[0070] It should be noted that the executing entity in this embodiment can be a computing service device with data processing, network communication, and program execution functions, such as a tablet computer, personal computer, or mobile phone, or an electronic device capable of performing the above functions, such as a wind turbine generator control device based on dynamic yaw optimization. This embodiment does not specifically limit it in this regard. The following uses a wind turbine generator control device based on dynamic yaw optimization as an example to describe this embodiment and the following embodiments.

[0071] All actions involving the acquisition of signals, information, or data in this application are carried out in accordance with the relevant data protection laws and policies of the country where the application is located, and with the authorization of the owner of the relevant device.

[0072] This application provides a wind turbine control method based on dynamic yaw optimization, referring to... Figure 1 , Figure 1 This is a flowchart illustrating the first embodiment of the wind turbine control method based on dynamic yaw optimization in this application.

[0073] In this embodiment, the wind turbine control method based on dynamic yaw optimization includes steps S10~S40:

[0074] Step S10: Using a lidar installed on the wind turbine nacelle, wind speed and direction are measured at forward measurement points in front of the wind turbine to obtain forward wind information.

[0075] It should be noted that forward wind information is a set of wind condition data obtained by lidar at forward measurement points, which is about the wind conditions that will reach the wind turbine location in the near future.

[0076] Understandably, by emitting laser beams at multiple predefined measurement points tens to hundreds of meters in front of the wind turbine and analyzing the signals reflected back by aerosol particles in the air based on the Doppler frequency shift principle, the three-dimensional velocity vector of the wind field can be accurately measured. This active remote sensing technology overcomes the inherent delay in measuring historical winds with traditional nacelle anemometers, enabling the capture of key information such as wind speed, wind direction, and turbulence intensity tens of seconds in advance before the wind turbine hub arrives. The acquired real-time forward-looking wind field data, after preprocessing such as filtering and coordinate transformation, provides high-quality input for subsequent power and load prediction models, forming the data foundation for predictive yaw control, improving wind energy capture efficiency, and reducing mechanical loads.

[0077] In one feasible implementation, the step of measuring wind speed and direction at forward measurement points in front of the wind turbine using a lidar installed on the wind turbine nacelle to obtain forward wind information includes:

[0078] Based on the rotor diameter and rated wind speed of the wind turbine, determine the target look-ahead measurement points;

[0079] The lidar installed on the wind turbine nacelle is used to continuously scan the target forward measurement point, collect multiple sets of wind speed and wind direction data, and generate wind information.

[0080] The wind information is preprocessed to obtain forward wind information.

[0081] It should be noted that rotor diameter refers to the diameter of the circle swept by the rotating blades of a wind turbine. It is a key parameter measuring the area of ​​wind energy captured by the turbine and directly affects its power output. Rated wind speed refers to the minimum wind speed required for a wind turbine to reach its rated power output.

[0082] Understandably, the rotor diameter determines the size of the airflow area affected by the wind turbine, while the rated wind speed characterizes the most critical operating range. Based on the core characteristics of the wind turbine itself, target look-ahead measurement points are determined to ensure that the measurement locations provide sufficient warning time while accurately reflecting the actual wind conditions about to affect the wind turbine. Next, an airborne lidar continuously scans these preset points, dynamically collecting massive amounts of raw wind speed and direction data to generate a raw dataset of wind power information containing spatiotemporal information. Finally, through preprocessing operations such as coordinate transformation, filtering and denoising, and data alignment, outliers caused by atmospheric turbulence or equipment noise are removed, and the data is unified into a coordinate system compatible with the wind turbine control system. This ultimately outputs high-quality look-ahead wind information that can be directly used for model prediction, laying a reliable data foundation for the entire optimization control process.

[0083] In one feasible implementation, the step of determining the target look-ahead measurement point based on the rotor diameter and rated wind speed of the wind turbine includes:

[0084] The initial look-ahead measurement point is determined based on the rotor diameter of the wind turbine.

[0085] Wind shear information is determined based on the geographical environment information and turbulence information of the location of the wind turbine.

[0086] The initial look-ahead measurement points are optimized based on the rated wind speed of the wind turbine and the wind shear information, and the optimized look-ahead measurement points are determined as the target look-ahead measurement points.

[0087] It should be noted that the initial look-ahead measurement point refers to the location of the measurement point initially set based on the wind turbine rotor diameter, usually an empirical or theoretically calculated value. Geographical environmental information refers to the topography, landforms, surface roughness, and surrounding obstacles of the wind turbine installation site. These factors significantly affect the flow characteristics of near-surface winds. Turbulence information refers to the physical quantities describing the intensity and frequency of random and irregular changes in wind speed and direction over a short period. High turbulence implies more complex and unstable wind conditions. Wind shear information refers to the data model of wind speed and direction changes with altitude. Within the boundary layer, due to surface friction, wind speed typically increases with altitude, and wind direction may also deflect.

[0088] Understandably, the initial look-ahead measurement point with a reasonable warning time is first determined based on the rotor diameter of the wind turbine, for example, at a distance of 2-3 times the rotor diameter. However, to improve control accuracy, specific wind field environmental data of the wind turbine's location is further introduced, namely geographical environmental information, such as complex hills or flat grasslands, and real-time turbulence information. Accurate wind shear information is calculated using fluid dynamics models or data-driven models, thereby quantifying the changes in speed and direction of wind as it propagates from the measurement point to the wind turbine. Finally, the control system combines the wind turbine's rated wind speed with the calculated wind shear effect to dynamically optimize and adjust the distance and height of the initial measurement point. For example, in strong wind shear environments, the height of the measurement point may need to be adjusted to more accurately represent the average wind speed acting on the entire rotor; in highly turbulent areas, the measurement distance may be appropriately shortened to improve data reliability and signal-to-noise ratio. Through this closed-loop optimization process, the final target forward measurement points can reflect the effective wind conditions that will affect the wind turbine to the greatest extent, providing the highest quality data source for subsequent prediction and control, and demonstrating the technological advancement from rough estimation to precise perception.

[0089] Step S20: Real-time acquisition of the wind turbine's current yaw angle, current wind speed, generator power output, and nacelle acceleration;

[0090] It should be noted that the current yaw angle refers to the angle between the nacelle axis of the wind turbine and the current actual wind direction. The current wind speed refers to the wind speed value measured instantly at the turbine's current position by an anemometer installed on the top or rear of the nacelle. Generator power output refers to the actual electrical power delivered by the wind turbine to the grid at a specific moment. Nacelle acceleration refers to the rate of change of motion of the nacelle in a specific direction, particularly in the left and right yaw directions, as measured by acceleration sensors installed inside the nacelle.

[0091] Understandably, this is achieved through multiple sensor data streams within the integrated Supervisory Control and Data Acquisition (SCADA) system. The current yaw angle, provided by the yaw system's encoder, directly reflects the turbine's alignment with the wind direction and is crucial for assessing wind energy capture efficiency and determining whether yaw is necessary. The current wind speed, measured by the nacelle anemometer, while reflecting past wind conditions, directly drives the turbine's current load and output power, serving as a vital benchmark for verifying the accuracy of forward wind predictions, calibrating power curves, and assessing structural safety. Generator power output, as the final performance indicator, directly reflects wind condition changes and turbine control effectiveness, serving as a core basis for evaluating the effectiveness of any optimization control action. Nacelle acceleration, monitored in real-time by wind shear, turbulence, and the yaw action itself, provides real-time feedback on mechanical vibrations and dynamic loads, ensuring turbine structural safety and optimizing load. The simultaneous acquisition of these four data types constitutes a complete information set describing the turbine's state and environment.

[0092] In one feasible implementation, the step of acquiring the current yaw angle, current wind speed, generator power output, and nacelle acceleration of the wind turbine in real time includes:

[0093] The original yaw angle, original wind speed, original generator power output, and original nacelle acceleration of the wind turbine are collected in real time.

[0094] The original yaw angle, original wind speed, original generator power output, and original nacelle acceleration are corrected to obtain corrected data.

[0095] The corrected data is subjected to a consistency check. If the consistency check is successful, the corrected data is determined as the current yaw angle, current wind speed, generator power output, and nacelle acceleration.

[0096] It should be noted that raw data refers to initial measurement values ​​read directly from the sensor or control system interface without any processing. This data may contain measurement errors, noise, transient interference, or inconsistencies in format.

[0097] Understandably, the system collects raw data in real time from sensors such as the yaw encoder, nacelle anemometer, generator power transmitter, and nacelle accelerometer. However, this raw data is mixed with high-frequency electronic noise, inherent sensor errors, or transient pulse interference. Therefore, the system performs data correction on the raw data. For example, it applies a low-pass digital filter to smooth the wind speed and acceleration signals to remove invalid high-frequency fluctuations, linearizes the power output according to the calibration curve, or converts the yaw angle signal into a standardized angle value. Finally, the system performs cross-validation based on the wind turbine's physical model and operating logic. For example, it checks whether the current wind speed and generator power output are within a reasonable power curve range; analyzes whether the nacelle acceleration shows a corresponding trend when the yaw angle changes; and determines whether the forward wind speed from the lidar has a reasonable correlation with the historical wind speed from the nacelle anemometer. Only when all data passes this internal consistency and physical rationality test are they finally confirmed as valid current state data that can be used for advanced control algorithms.

[0098] Step S30: Based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration, predict the target yaw angle of the wind turbine within the prediction time window;

[0099] It should be noted that the prediction time window refers to a period of time extending into the future from the current moment. Within this window, the system needs to predict and control the yaw angle of the wind turbine. Its length typically depends on the size of the wind turbine, its dynamic response characteristics, and the effective range of the look-ahead measurement. The target yaw angle refers to the yaw angle that the system, through prediction calculations, believes should be achieved at a certain future moment to optimize the wind turbine's performance.

[0100] Understandably, the initial step involves fusing forward wind information with the current yaw angle, current wind speed, generator power output, and nacelle acceleration. Current state data is used to calibrate the initial conditions of the prediction model and reflects the real-time dynamic characteristics of the wind turbine. For example, the current nacelle acceleration can indicate the instantaneous load on the rotor and the vibration of the structure. The system includes a dynamic model of the wind turbine. This model simulates the response of the turbine's yaw system, aerodynamic performance, and structural dynamics under a given forward wind. Rolling optimization calculations are performed within this prediction time window to determine the target yaw angle of the wind turbine within that window.

[0101] In one feasible implementation, the step of predicting the target yaw angle of the wind turbine within the prediction time window based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration includes:

[0102] The forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration are time-series aligned, and the aligned data is used as the input vector.

[0103] Within the prediction time window, based on the current unit status and the input vector, the predicted power generation and key component load of the wind turbine are predicted.

[0104] The target yaw angle is determined based on the predicted power generation and the load on the key components.

[0105] It should be noted that time series alignment refers to the process of unifying data from different sensors with different timestamps or sampling frequencies onto the same time reference through techniques such as interpolation, synchronization, or resampling. The input vector is a multi-dimensional data set composed of all relevant input parameters. Predicted power generation is the expected electrical power output of a wind turbine under a specific control strategy within the prediction time window. Critical component loads refer to the mechanical stresses or moments that the core structure of the wind turbine (such as blades, main shaft, gearbox, and tower) is expected to experience within the prediction time window.

[0106] Understandably, referring to Figure 2 , Figure 2 This is a schematic diagram of wind turbine yaw. First, the forward wind information, current yaw angle, current wind speed, generator power output, and nacelle acceleration are time-series aligned. The aligned data is then used as an input vector, represented as:

[0107] .

[0108] in, To align to time Forward-looking information on wind, The current yaw angle after alignment. The current wind speed after alignment. To achieve the aligned generator power output, The acceleration of the aligned cabin.

[0109] Based on the current unit status Yaw angle sequence to be optimized and forward-looking information As input, for the entire prediction time domain ,Right now From to During the time period, parallel computing is used to predict the power generation sequence and the load sequence of key components.

[0110] Predicted power generation sequence:

[0111]

[0112] in, It is the aerodynamic-power generation mapping function in the model.

[0113] Key component load sequence:

[0114]

[0115] in, It is the aerodynamic-power generation mapping function in the model.

[0116] Then, based on the predicted power generation and the load on key components, the target yaw angle is determined, which can be described as:

[0117]

[0118] in, To maximize power generation, To minimize fatigue load, To smooth out the yaw action.

[0119] And it satisfies the following constraints:

[0120] Yaw angle range:

[0121] Yaw rate limit:

[0122] Based on this, Yaw angle for the target.

[0123] In one feasible implementation, the step of aligning the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration in a time series, and using the aligned data as an input vector, includes:

[0124] The timestamps of the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration are determined respectively, and the timestamps are mapped to the standard time base respectively to obtain the aligned timestamps;

[0125] Arrange the data based on the alignment timestamps, determine the missing data positions, and determine the preceding and following data positions of the missing data positions;

[0126] The data filling value is determined based on the preceding data and the following data;

[0127] The data filler values ​​are used to fill the missing data positions to obtain aligned data;

[0128] An input vector is generated based on the alignment data.

[0129] It should be noted that a timestamp refers to the specific point in time when each piece of data is collected or generated. A standard time base refers to a unified, high-precision time reference system, such as Coordinated Universal Time (UTC) or the internal high-precision clock of a wind turbine main control system.

[0130] Understandably, the first step is to uniformly map the timestamps of all data to a high-precision standard time base, such as the clock of the main control PLC, thereby establishing a unified virtual time axis. Subsequently, the system will identify gaps on this time axis caused by different sampling rates or data loss, and use interpolation algorithms, such as linear interpolation, to calculate reasonable fill values ​​using the preceding and following valid sample values ​​adjacent to that position. This ensures that a complete and time-synchronized input vector can be formed at each control cycle node, providing a reliable and consistent data foundation for the accurate calculation of subsequent model predictive control.

[0131] In one feasible implementation, the step of predicting the predicted power generation and critical component load of the wind turbine based on the current unit status and the input vector within the prediction time window includes:

[0132] Within the forecast time window, the adjustment step size of the yaw angle is determined based on the current crew status;

[0133] Based on the adjusted step size, a rolling simulation is performed on the input vector to obtain the simulation results;

[0134] Based on the simulation results, the predicted power generation and key component load sequence corresponding to each time step and each candidate yaw angle within the prediction time window are determined.

[0135] In the implementation, a reasonable yaw angle adjustment step size is first dynamically determined based on the current unit status to avoid unrealistic searches and ensure computational efficiency. Then, using the currently aligned input vector as initial conditions, the controller performs high-fidelity dynamic model simulations of a series of candidate yaw angle trajectories generated according to this step size within the future prediction time window. Each rolling simulation calculates the aerodynamic power and structural load of the wind turbine at each discrete time step under that specific yaw strategy, ultimately generating a complete prediction sequence to provide quantitative basis for the next step of multi-objective optimization decision-making.

[0136] In one feasible implementation, the step of determining the target yaw angle based on the predicted power generation and the load on the critical component includes:

[0137] A comprehensive evaluation function is constructed by taking the cumulative predicted power generation within the predicted time window as the positive term and the weighted cumulative value of the load of key components as the negative term.

[0138] Traverse the candidate yaw angle strategies and determine the comprehensive evaluation function value corresponding to the candidate yaw angle strategy based on the comprehensive evaluation function;

[0139] The candidate yaw angle strategy corresponding to the largest comprehensive evaluation function value among the comprehensive evaluation function values ​​is determined as the target yaw angle strategy, and the target yaw angle is determined according to the target yaw angle strategy.

[0140] In the specific implementation, firstly, a comprehensive evaluation function is constructed that balances power generation revenue and mechanical losses. The total power generation within the prediction period is used as the positive revenue term, while the cumulative load on key components such as the tower and blades is used as the negative revenue term representing equipment fatigue damage. Then, the optimizer iterates through all candidate yaw strategies pre-generated through simulation, calculating the corresponding comprehensive evaluation function value for each. Finally, an optimization algorithm selects the candidate strategy that maximizes the evaluation function, thus achieving the goal of effectively controlling the load while increasing power generation. The initial yaw angle command corresponding to this strategy is then used as the optimal decision output for the current control period.

[0141] Step S40: Generate a yaw control command based on the target yaw angle, and send the yaw control command to the yaw drive system to enable the wind turbine to control the wind ahead of the wind.

[0142] It should be noted that the yaw control command is a specific operation signal generated by the main control system, which usually includes the target yaw angle, yaw speed, direction (clockwise / counterclockwise) and necessary safety verification information.

[0143] Understandably, when controlling a wind turbine, the optimized target yaw angle can be compared with the current measured yaw angle of the nacelle. Based on the preset yaw speed curve and dead zone range, a concrete yaw control command with direction and speed information is generated. This command is sent in real time to the frequency converter of the yaw drive system via a fieldbus (such as CANopen or PROFIBUS). The frequency converter drives the yaw motor to operate, and the braking system releases or clamps according to the command, thereby driving the nacelle to rotate smoothly to the target angle. This achieves advanced, smooth, and precise tracking of wind direction changes, ultimately achieving the control goals of improving power generation efficiency and reducing load.

[0144] In one feasible implementation, the step of generating yaw control commands based on the target yaw angle includes:

[0145] Calculate the angular deviation between the target yaw angle and the current actual yaw angle of the wind turbine;

[0146] Based on the angle deviation, preset yaw rate, and preset acceleration limit, a yaw motor control signal is generated;

[0147] The yaw motor control signal is encapsulated using a standard communication protocol to obtain the yaw control command.

[0148] It should be noted that the angle deviation refers to the difference between the target yaw angle and the current actual orientation of the wind turbine nacelle. Preset yaw rate and preset acceleration limits are safety and performance parameters to prevent excessive rotation and ensure the safety and stability of the wind turbine.

[0149] In practical implementation, the key step in translating high-level angle commands into low-level executable commands is as follows: The controller first calculates the real-time deviation between the target and the current yaw angle. Then, based on the magnitude and direction of this deviation, and in conjunction with preset rate and acceleration curves, it generates a smooth, shock-free yaw motor speed or torque setpoint signal. Next, this low-level drive signal is packaged according to a predetermined industrial communication protocol, with the addition of necessary information such as control words and status checks, ultimately forming a complete yaw control command, which is sent to the yaw drive inverter for execution via a digital bus.

[0150] This embodiment provides a wind turbine control method based on dynamic yaw optimization. A lidar mounted on the wind turbine nacelle measures wind speed and direction at a forward measurement point in front of the turbine, acquiring forward wind information. The method also acquires the wind turbine's current yaw angle, current wind speed, generator power output, and nacelle acceleration in real time. Based on this forward wind information, current yaw angle, current wind speed, generator power output, and nacelle acceleration, the method predicts the target yaw angle of the wind turbine within the prediction time window. According to the target yaw angle, a yaw control command is generated and sent to the yaw drive system to enable the wind turbine to proactively control the wind. This method solves the technical problems of low wind energy capture efficiency and high turbine load caused by yaw control response lag in existing technologies.

[0151] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the wind turbine control method based on dynamic yaw optimization in this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0152] This application also provides a wind turbine control system based on dynamic yaw optimization; please refer to [reference needed]. Figure 3 The wind turbine control system based on dynamic yaw optimization includes:

[0153] The wind sensing module 10 is used to measure wind speed and direction at forward measurement points in front of the wind turbine using a lidar installed on the wind turbine nacelle, and to obtain forward wind information.

[0154] The generator sensing module 20 is used to acquire the current yaw angle, current wind speed, generator power output and nacelle acceleration of the wind turbine in real time.

[0155] Yaw prediction module 30 is used to predict the target yaw angle of the wind turbine within the prediction time window based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration.

[0156] The yaw control module 40 is used to generate a yaw control command based on the target yaw angle and send the yaw control command to the yaw drive system so as to enable the wind turbine to control the wind ahead of the wind.

[0157] In one feasible implementation, the yaw prediction module 30 is further configured to perform time series alignment of the forward wind information, the current yaw angle, the current wind speed, the generator power output and the nacelle acceleration, and use the aligned data as an input vector;

[0158] Within the prediction time window, based on the current unit status and the input vector, the predicted power generation and key component load of the wind turbine are predicted.

[0159] The target yaw angle is determined based on the predicted power generation and the load on the key components.

[0160] In one feasible implementation, the yaw prediction module 30 is further configured to determine the timestamps of the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration, respectively, and map the timestamps to a standard time reference to obtain aligned timestamps.

[0161] Arrange the data based on the alignment timestamps, determine the missing data positions, and determine the preceding and following data positions of the missing data positions;

[0162] The data filling value is determined based on the preceding data and the following data;

[0163] The data filler values ​​are used to fill the missing data positions to obtain aligned data;

[0164] An input vector is generated based on the alignment data.

[0165] In one feasible implementation, the yaw prediction module 30 is further configured to determine the adjustment step size of the yaw angle based on the current crew status within the prediction time window.

[0166] Based on the adjusted step size, a rolling simulation is performed on the input vector to obtain the simulation results;

[0167] Based on the simulation results, the predicted power generation and key component load sequence corresponding to each time step and each candidate yaw angle within the prediction time window are determined.

[0168] In one feasible implementation, the yaw prediction module 30 is further configured to construct a comprehensive evaluation function with the cumulative predicted power generation within the prediction time window as a positive term and the weighted cumulative value of the load of key components as a negative term.

[0169] Traverse the candidate yaw angle strategies and determine the comprehensive evaluation function value corresponding to the candidate yaw angle strategy based on the comprehensive evaluation function;

[0170] The candidate yaw angle strategy corresponding to the largest comprehensive evaluation function value among the comprehensive evaluation function values ​​is determined as the target yaw angle strategy, and the target yaw angle is determined according to the target yaw angle strategy.

[0171] In one feasible implementation, the yaw control module 40 is further configured to calculate the angle deviation between the target yaw angle and the current actual yaw angle of the wind turbine.

[0172] Based on the angle deviation, preset yaw rate, and preset acceleration limit, a yaw motor control signal is generated;

[0173] The yaw motor control signal is encapsulated using a standard communication protocol to obtain the yaw control command.

[0174] In one feasible implementation, the generator sensing module 20 is also used to collect the original yaw angle, original wind speed, original generator power output and original nacelle acceleration of the wind turbine in real time.

[0175] The original yaw angle, original wind speed, original generator power output, and original nacelle acceleration are corrected to obtain corrected data.

[0176] The corrected data is subjected to a consistency check. If the consistency check is successful, the corrected data is determined as the current yaw angle, current wind speed, generator power output, and nacelle acceleration.

[0177] In one feasible implementation, the wind sensing module 10 is further configured to determine the target forward measurement point based on the rotor diameter and rated wind speed of the wind turbine.

[0178] The lidar installed on the wind turbine nacelle is used to continuously scan the target forward measurement point, collect multiple sets of wind speed and wind direction data, and generate wind information.

[0179] The wind information is preprocessed to obtain forward wind information.

[0180] In one feasible implementation, the wind sensing module 10 is further configured to determine an initial look-ahead measurement point based on the rotor diameter of the wind turbine.

[0181] Wind shear information is determined based on the geographical environment information and turbulence information of the location of the wind turbine.

[0182] The initial look-ahead measurement points are optimized based on the rated wind speed of the wind turbine and the wind shear information, and the optimized look-ahead measurement points are determined as the target look-ahead measurement points.

[0183] The wind turbine control system based on dynamic yaw optimization provided in this application, employing the wind turbine control method based on dynamic yaw optimization in the above embodiments, can solve the technical problems of low wind energy capture efficiency and high unit load caused by yaw control response lag. Compared with the prior art, the beneficial effects of the wind turbine control system based on dynamic yaw optimization provided in this application are the same as those of the wind turbine control method based on dynamic yaw optimization provided in the above embodiments, and other technical features in the wind turbine control system based on dynamic yaw optimization are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0184] This application provides a wind turbine control device based on dynamic yaw optimization. The wind turbine control device based on dynamic yaw optimization includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute the wind turbine control method based on dynamic yaw optimization in the above embodiment 1.

[0185] The following is for reference. Figure 4 This document illustrates a structural schematic diagram of a wind turbine control device suitable for implementing the dynamic yaw optimization-based embodiments of this application. The dynamic yaw optimization-based wind turbine control device in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 4 The wind turbine control device based on dynamic yaw optimization shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0186] like Figure 4 As shown, the wind turbine control device based on dynamic yaw optimization may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in ROM (Read Only Memory) 1002 or the program loaded from storage device 1003 into RAM (Random Access Memory) 1004. RAM 1004 also stores various programs and data required for the operation of the wind turbine control device based on dynamic yaw optimization. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via bus 1005. Input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to I / O interface 1006: input devices 1007 including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices 1008 including, for example, LCDs (Liquid Crystal Displays), speakers, vibrators, etc.; storage devices 1003 including, for example, magnetic tapes, hard disks, etc.; and communication devices 1009. Communication device 1009 allows the wind turbine control equipment based on dynamic yaw optimization to communicate wirelessly or wiredly with other devices to exchange data. Although the figure shows a wind turbine control equipment based on dynamic yaw optimization with various systems, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems can be implemented alternatively.

[0187] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0188] The wind turbine control device based on dynamic yaw optimization provided in this application, employing the wind turbine control method based on dynamic yaw optimization in the above embodiments, can solve the technical problems of wind turbine control based on dynamic yaw optimization. Compared with the prior art, the beneficial effects of the wind turbine control device based on dynamic yaw optimization provided in this application are the same as those of the wind turbine control method based on dynamic yaw optimization provided in the above embodiments, and other technical features in this wind turbine control device based on dynamic yaw optimization are the same as those disclosed in the previous embodiment method, and will not be repeated here.

[0189] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0190] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0191] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, which are used to execute the wind turbine control method based on dynamic yaw optimization in the above embodiments.

[0192] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, RAM (Random Access Memory), ROM (Read Only Memory), EPROM (Erasable Programmable Read Only Memory or Flash Memory), optical fibers, CD-ROM (CD-Read Only Memory), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, system, or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0193] The aforementioned computer-readable storage medium may be included in a wind turbine control device based on dynamic yaw optimization; or it may exist independently and not assembled into a wind turbine control device based on dynamic yaw optimization.

[0194] The aforementioned computer-readable storage medium carries one or more programs that, when executed by a wind turbine control device based on dynamic yaw optimization, cause the wind turbine control device based on dynamic yaw optimization to: measure wind speed and wind direction at a forward measurement point in front of the wind turbine using a lidar installed on the wind turbine nacelle, thereby acquiring forward wind information.

[0195] The current yaw angle, current wind speed, generator power output, and nacelle acceleration of the wind turbine are acquired in real time.

[0196] Based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration, the target yaw angle of the wind turbine within the prediction time window is predicted.

[0197] Based on the target yaw angle, a yaw control command is generated and sent to the yaw drive system to enable the wind turbine to control the wind ahead of the wind.

[0198] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including LAN (Local Area Network) or WAN (Wide Area Network)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0199] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0200] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0201] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described wind turbine control method based on dynamic yaw optimization, and can solve the technical problem of wind turbine control based on dynamic yaw optimization. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as the beneficial effects of the wind turbine control method based on dynamic yaw optimization provided in the above embodiments, and will not be repeated here.

[0202] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the wind turbine control method based on dynamic yaw optimization as described above.

[0203] The computer program product provided in this application can solve the technical problem of wind turbine control based on dynamic yaw optimization. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the wind turbine control method based on dynamic yaw optimization provided in the above embodiments, and will not be repeated here.

[0204] The above are only some embodiments of this application and do not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the content of this application specification and drawings, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A wind turbine control method based on dynamic yaw optimization, characterized in that, The wind turbine control method based on dynamic yaw optimization includes: By using lidar installed on the nacelle of a wind turbine, wind speed and direction are measured at forward measurement points in front of the turbine to obtain forward wind information. The current yaw angle, current wind speed, generator power output, and nacelle acceleration of the wind turbine are acquired in real time. Based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration, the target yaw angle of the wind turbine within the prediction time window is predicted. Based on the target yaw angle, a yaw control command is generated and sent to the yaw drive system so that the wind turbine can control the wind ahead of time. The step of predicting the target yaw angle of the wind turbine within the prediction time window based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration includes: The forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration are time-series aligned, and the aligned data is used as the input vector. Within the prediction time window, based on the current unit status and the input vector, the predicted power generation and key component load of the wind turbine are predicted. The target yaw angle is determined based on the predicted power generation and the load on the key components. The step of determining the target yaw angle based on the predicted power generation and the load of the key components includes: A comprehensive evaluation function is constructed by taking the cumulative predicted power generation within the predicted time window as the positive term and the weighted cumulative value of the load of key components as the negative term. Traverse the candidate yaw angle strategies and determine the comprehensive evaluation function value corresponding to the candidate yaw angle strategy based on the comprehensive evaluation function; The candidate yaw angle strategy corresponding to the largest comprehensive evaluation function value among the comprehensive evaluation function values ​​is determined as the target yaw angle strategy, and the target yaw angle is determined according to the target yaw angle strategy.

2. The method as described in claim 1, characterized in that, The step of aligning the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration in a time series, and using the aligned data as an input vector, includes: The timestamps of the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration are determined respectively, and the timestamps are mapped to the standard time base respectively to obtain the aligned timestamps; Arrange the data based on the alignment timestamps, determine the missing data positions, and determine the preceding and following data positions of the missing data positions; The data filling value is determined based on the preceding data and the following data; The data filler values ​​are used to fill the missing data positions to obtain aligned data; An input vector is generated based on the alignment data.

3. The method as described in claim 1, characterized in that, The steps for predicting the predicted power generation and critical component load of the wind turbine based on the current unit status and the input vector within the prediction time window include: Within the forecast time window, the adjustment step size of the yaw angle is determined based on the current crew status; Based on the adjusted step size, a rolling simulation is performed on the input vector to obtain the simulation results; Based on the simulation results, the predicted power generation and key component load sequence corresponding to each time step and each candidate yaw angle within the prediction time window are determined.

4. The method as described in claim 1, characterized in that, The step of generating yaw control commands based on the target yaw angle includes: Calculate the angular deviation between the target yaw angle and the current actual yaw angle of the wind turbine; Based on the angle deviation, preset yaw rate, and preset acceleration limit, a yaw motor control signal is generated; The yaw motor control signal is encapsulated using a standard communication protocol to obtain the yaw control command.

5. The method as described in claim 1, characterized in that, The steps for acquiring the current yaw angle, current wind speed, generator power output, and nacelle acceleration of the wind turbine in real time include: The original yaw angle, original wind speed, original generator power output, and original nacelle acceleration of the wind turbine are collected in real time. The original yaw angle, original wind speed, original generator power output, and original nacelle acceleration are corrected to obtain corrected data. The corrected data is subjected to a consistency check. If the consistency check is successful, the corrected data is determined as the current yaw angle, current wind speed, generator power output, and nacelle acceleration.

6. The method as described in claim 1, characterized in that, The steps of measuring wind speed and direction at forward measurement points in front of the wind turbine using a lidar installed on the wind turbine nacelle to obtain forward wind information include: Based on the rotor diameter and rated wind speed of the wind turbine, determine the target look-ahead measurement points; The lidar installed on the nacelle of the wind turbine is used to continuously scan the target forward measurement point, collect multiple sets of wind speed and wind direction data, and generate wind information. The wind information is preprocessed to obtain forward wind information.

7. The method as described in claim 6, characterized in that, The steps for determining the target look-ahead measurement point based on the rotor diameter and rated wind speed of the wind turbine include: The initial look-ahead measurement point is determined based on the rotor diameter of the wind turbine. Wind shear information is determined based on the geographical environment information and turbulence information of the location of the wind turbine. The initial look-ahead measurement points are optimized based on the rated wind speed of the wind turbine and the wind shear information, and the optimized look-ahead measurement points are determined as the target look-ahead measurement points.

8. A wind turbine control system based on dynamic yaw optimization, characterized in that, The wind turbine control system based on dynamic yaw optimization includes: The wind sensing module is used to measure wind speed and direction at forward measurement points in front of the wind turbine using a lidar installed on the nacelle of the wind turbine, thereby obtaining forward wind information. The generator sensing module is used to acquire the current yaw angle, current wind speed, generator power output and nacelle acceleration of the wind turbine in real time. The yaw prediction module is used to predict the target yaw angle of the wind turbine within the prediction time window based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration. The yaw control module is used to generate yaw control commands based on the target yaw angle and send the yaw control commands to the yaw drive system so that the wind turbine can control the wind ahead of time. The step of predicting the target yaw angle of the wind turbine within the prediction time window based on the forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration includes: The forward wind information, the current yaw angle, the current wind speed, the generator power output, and the nacelle acceleration are time-series aligned, and the aligned data is used as the input vector. Within the prediction time window, based on the current unit status and the input vector, the predicted power generation and key component load of the wind turbine are predicted. The target yaw angle is determined based on the predicted power generation and the load on the key components. The step of determining the target yaw angle based on the predicted power generation and the load of the key components includes: A comprehensive evaluation function is constructed by taking the cumulative predicted power generation within the predicted time window as the positive term and the weighted cumulative value of the load of key components as the negative term. Traverse the candidate yaw angle strategies and determine the comprehensive evaluation function value corresponding to the candidate yaw angle strategy based on the comprehensive evaluation function; The candidate yaw angle strategy corresponding to the largest comprehensive evaluation function value among the comprehensive evaluation function values ​​is determined as the target yaw angle strategy, and the target yaw angle is determined according to the target yaw angle strategy.

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