Protection control method and device for wind turbine generator additionally provided with laser radar

Abstract

The invention provides a protection control method and device for a wind turbine generator additionally provided with a laser radar. According to the protection control method for the wind turbine generator additionally provided with the laser radar, the operation safety and reliability of the wind turbine generator under the extreme wind condition can be remarkably improved, dynamic loads of key components are effectively reduced through prospective wind speed measurement of the laser radar, and the service life of the wind turbine generator is prolonged.

Description

Technical Field

[0001] This invention relates to the field of wind turbine protection and control technology, and in particular to a wind turbine protection and control method and device equipped with lidar. Background Technology

[0002] Wind turbine protection and control, as a core component of the safe operation of wind power systems, is widely used in the field of extreme weather protection for offshore wind farms. With the development of wind power technology towards larger megawatts and deeper waters, traditional protection systems have evolved into a technical system comprised of mechanical anemometers, ultrasonic anemometers, and yaw correction systems. Specifically, this system covers the entire process from wind condition perception to protection execution, including key aspects such as wind speed measurement behind the nacelle, flow field distortion compensation, and threshold judgment logic. Among these, mechanical anemometers measure wind speed through a cup structure, ultrasonic anemometers utilize the propagation characteristics of sound waves for non-contact measurement, and lidar technology achieves three-dimensional reconstruction of the wind field ahead through the Doppler effect. While lidar applications have evolved from early flow field visualization to current yaw correction optimization, its synergistic mechanism in system-level protection and control has not yet been established.

[0003] However, existing wind turbine protection methods directly utilize measurement data from sensors behind the nacelle, failing to fully consider the systematic impact of flow field distortion on wind speed perception. Specifically, mechanical anemometers suffer from a 3-5 second response delay and are susceptible to icing interference, while ultrasonic anemometers generate measurement errors of 0.5-1.2 m / s under vibration. Both are limited by the flow field distortion effect in the turbine wake region. Consequently, traditional protection systems often experience a 25-35% increase in blade root load and an 18-22% increase in tower base bending moment under extreme wind conditions such as typhoons due to information lag. While existing lidar can achieve forward-looking measurements of the wind field (detection distances up to hundreds of meters), its application is limited to yaw correction and feedforward control, failing to establish a dynamic protection threshold system based on forward wind speed prediction. This technological limitation restricts the survivability of the turbines in extreme weather conditions, posing a significant risk of major accidents such as blade breakage and tower overturning due to delayed protection actions, severely restricting the safe operation of offshore wind farms. Summary of the Invention

[0004] The present invention aims to at least partially solve one of the technical problems in the related art.

[0005] Therefore, the first objective of this invention is to propose a protection and control method for wind turbine generators equipped with lidar.

[0006] The second objective of this invention is to provide a wind turbine protection and control device equipped with a lidar.

[0007] To achieve the above objectives, a first aspect of the present invention provides a protection and control method for wind turbine generators equipped with lidar, comprising: S1 acquires wind speed data simultaneously through an ultrasonic anemometer, a mechanical anemometer, and a forward-looking scanning lidar installed on the top of the cabin, obtaining cabin wind speed signals and lidar wind speed signals. S2, calculate the instantaneous average, short-term average and long-term average of the current wind speed based on the nacelle wind speed signal, and at the same time convert the lidar wind speed signal into wind speed data that matches the distance and height of the wind turbine, and calculate the instantaneous average, short-term average and long-term average of the lidar wind speed. S3, when the long-term average value of the nacelle wind speed signal or the long-term average value of the lidar wind speed signal exceeds the preset wind speed threshold for prohibiting startup, the unit is prohibited from starting and remains in standby mode. S4. When the unit is in grid-connected operation, based on the combined judgment results of the instantaneous average value, short-term average value and long-term average value of the nacelle wind speed signal and the lidar wind speed signal, when the average wind speed in any time window exceeds the corresponding cut-out wind speed threshold, a feathering command is sent to the pitch system and the unit is controlled to stop and cut out.

[0008] In one embodiment of the present invention, S1 includes: S11 calculates the three-dimensional wind speed vector of the wind field ahead by using the Doppler frequency shift of the lidar, and performs vertical wind shear correction by combining the wind turbine height distribution; S12 uses a Kalman filter algorithm to perform real-time noise reduction on the voltage signals of ultrasonic and mechanical anemometers, with a filtering frequency range of 0.1-20Hz.

[0009] In one embodiment of the present invention, S2 includes: S21, A weighted average algorithm is used to fuse the cabin wind speed signal and the lidar wind speed signal, wherein the weighting coefficient of the lidar wind speed signal is 0.6-0.8; S22, a wind speed propagation time model is established based on the wind turbine rotation radius and lidar detection distance. The lidar wind speed signal is dynamically compensated according to the propagation time and then the average value is calculated.

[0010] In one embodiment of the present invention, S3 includes: S31, when the long-term average value of the lidar wind speed signal exceeds the prohibition threshold for starting, the yaw system is activated to adjust the orientation of the unit to avoid extreme wind directions; S32, if the long-term average difference between the cabin wind speed signal and the lidar wind speed signal exceeds 1.5 m / s, the sensor anomaly detection process is triggered and the lidar signal is used first for protection judgment.

[0011] In one embodiment of the present invention, S4 includes: S41, when the instantaneous average value exceeds the cut-out wind speed threshold, the response delay time for issuing a feathering command to the pitch system is less than 0.5 seconds; S42 dynamically adjusts the rate of change of the feathering angle based on the duration of the wind speed signal exceeding the threshold; the longer the duration, the lower the rate of change of the angle.

[0012] To achieve the above objectives, a second aspect of the present invention provides a wind turbine protection and control device equipped with a lidar, comprising: The synchronous data acquisition module is used to simultaneously acquire wind speed data through an ultrasonic anemometer, a mechanical anemometer, and a forward-looking scanning lidar installed on the top of the cabin, and to obtain cabin wind speed signals and lidar wind speed signals. The wind speed data processing module is used to calculate the instantaneous average, short-term average and long-term average of the current wind speed based on the nacelle wind speed signal, and at the same time convert the lidar wind speed signal into wind speed data that matches the distance and height of the wind turbine, and calculate the instantaneous average, short-term average and long-term average of the lidar wind speed. The standby state protection module is used to prevent the unit from starting and maintain the standby state when the long-term average value of the nacelle wind speed signal or the long-term average value of the lidar wind speed signal exceeds a preset wind speed threshold that prohibits starting the unit. The grid-connected operation protection module is used to, when the unit is in grid-connected operation, based on the combination of the instantaneous average value, short-term average value and long-term average value of the nacelle wind speed signal and the lidar wind speed signal, issue a feathering command to the pitch system and control the unit to stop and cut off when the average wind speed in any time window exceeds the corresponding cut-out wind speed threshold.

[0013] The method and apparatus of this invention, by adding lidar and fusing data from traditional wind speed sensors, enable wind turbines to achieve proactive early warning and graded protection control under extreme wind conditions, effectively reducing the dynamic load of the unit and the operational risks under extreme weather, and improving system safety and equipment lifespan.

[0014] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0015] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1This is a flowchart of a wind turbine protection and control method equipped with lidar according to an embodiment of the present invention; Figure 2 This is a flowchart of the wind turbine high wind warning and protection logic according to an embodiment of the present invention; Figure 3 This is a flowchart of the wind turbine high wind cut-out protection control logic according to an embodiment of the present invention; Figure 4 This is a logic flowchart of an alternative scheme for wind turbine high wind warning and protection according to an embodiment of the present invention; Figure 5 This is a structural diagram of a wind turbine protection and control device equipped with a lidar according to an embodiment of the present invention. Detailed Implementation

[0016] It should be noted that, unless otherwise specified, the embodiments and features described in the present invention can be combined with each other. The present invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0017] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0018] The following description, with reference to the accompanying drawings, describes a wind turbine protection and control method and device equipped with lidar according to an embodiment of the present invention.

[0019] Example 1 Figure 1 This is a flowchart of a protection and control method for a wind turbine equipped with lidar according to an embodiment of the present invention, as shown below. Figure 1 As shown, it includes: S1 acquires wind speed data simultaneously through an ultrasonic anemometer, a mechanical anemometer, and a forward-looking scanning lidar installed on the top of the cabin, obtaining cabin wind speed signals and lidar wind speed signals.

[0020] Specifically, this step involves simultaneously acquiring wind speed data using three types of wind speed measurement devices installed on the top of the wind turbine nacelle: an ultrasonic anemometer, a mechanical anemometer (such as a cup anemometer), and a forward-looking scanning lidar, to obtain nacelle wind speed signals and lidar wind speed signals. This step is the foundational step in the multi-source wind speed information fusion of this invention and plays a crucial role in subsequent protection and control logic.

[0021] Furthermore, ultrasonic anemometers calculate wind speed and direction by measuring the time difference of ultrasonic waves propagating in the air. Their measurement frequencies are typically above 10Hz, offering high response speed and measurement accuracy. Mechanical anemometers measure wind speed based on the linear relationship between the wind turbine's rotational speed and wind speed; their output is an analog voltage signal, requiring digital processing via an analog-to-digital converter (ADC). Forward-looking lidar uses the Doppler laser wind measurement principle, calculating the wind speed vector by emitting a laser beam in front of the wind turbine and receiving the backscattered signals from aerosol particles in the atmosphere. Its scanning range is typically 50 to 500 meters in front of the wind turbine, and the scanning frequency can be set from 1Hz to 10Hz, offering advantages such as non-contact operation, forward-looking capability, and high spatial resolution.

[0022] Furthermore, the measurement accuracy of ultrasonic anemometers is generally ±2 m / s, mechanical anemometers are ±3 m / s, while lidar can achieve an accuracy of ±1 m / s under ideal conditions. The signal processing unit needs to synchronously sample the raw data from the three sensors, and the time synchronization error should be controlled within ±10 ms to ensure the accuracy of data fusion. Simultaneously, the lidar wind speed signal needs to be interpolated based on the wind speed distribution at different distances and heights in front of the wind turbine to match the actual operating parameters of the wind turbine.

[0023] Furthermore, this step applies to wind speed monitoring of wind turbines in both standby and grid-connected operation. Especially under extreme weather conditions such as typhoons and hurricanes, lidar can detect the undisturbed raw wind speed ahead 10-30 seconds in advance, providing more accurate early warnings and cut-off criteria for the turbines.

[0024] Furthermore, by synchronously acquiring and processing multi-source wind speed signals, high-precision, multi-dimensional wind speed inputs are provided for subsequent protection and control logic, effectively improving the unit's response speed and control accuracy under extreme wind conditions, thereby significantly reducing the dynamic load on key components and improving operational safety and reliability.

[0025] Furthermore, S1 includes: S11 calculates the three-dimensional wind speed vector of the wind field ahead by using the Doppler frequency shift of the lidar, and performs vertical wind shear correction by combining the wind turbine height distribution.

[0026] Specifically, this step involves calculating the three-dimensional wind speed vector of the wind field ahead using the Doppler frequency shift of a lidar system, and performing vertical wind shear correction based on the wind turbine height distribution. This is the core technical aspect of the present invention for achieving forward-looking protection and control of wind turbine units. In some implementations, the lidar system adopts a forward-looking scanning mode, emitting multiple laser beams in front of the wind turbine at a fixed or adjustable scanning angle. By detecting the backscattering signals of aerosol particles in the atmosphere and combining the Doppler frequency shift principle, the three-dimensional wind speed information at multiple measurement points in the wind field is retrieved. Specifically, by measuring the Doppler frequency shift of aerosol particles under laser irradiation, the lidar system can construct a three-dimensional wind speed vector map of the wind field ahead through multi-angle scanning and multi-point measurement. The coverage area is typically 50m to 500m in front of the wind turbine, with a height resolution of up to 10m.

[0027] Furthermore, to improve the accuracy of wind speed prediction, the system needs to incorporate a geometric height distribution model of the wind turbine to perform vertical wind shear correction on the wind speed data measured by lidar. By mapping the wind speed measured by lidar to the height position of each blade of the wind turbine and applying a wind shear model for compensation, the overall wind speed load on the wind turbine can be more realistically reflected.

[0028] In practical applications, this step is particularly applicable to scenarios where wind turbines need to be restricted from starting or cut off in advance under extreme weather conditions such as typhoons and hurricanes. Through the fusion processing of three-dimensional wind speed vector and wind shear correction, the system can accurately predict wind speed change trends, thereby triggering protection mechanisms in advance when the unit is in standby or running state, effectively reducing the dynamic load on the blades, main shaft and tower, and improving the safety and reliability of unit operation.

[0029] S12 uses a Kalman filter algorithm to perform real-time noise reduction on the voltage signals of ultrasonic and mechanical anemometers, with a filtering frequency range of 0.1-20Hz.

[0030] Specifically, in the signal processing unit of this invention, the Kalman filter algorithm is used to perform real-time noise reduction processing on the voltage signals output by the ultrasonic anemometer and the mechanical anemometer. This is a key technical step for achieving multi-source wind speed information fusion and accurate judgment of protection control logic. This step, by constructing a dynamic system model and combining the statistical characteristics of sensor noise, optimizes the estimation of the original voltage signal, thereby effectively suppressing environmental noise and sensor errors, and improving the accuracy and stability of wind speed measurement.

[0031] In some implementations, the Kalman filter structure includes two stages: state prediction and measurement update. The system state variable can be defined as wind speed and its first derivative (i.e., the rate of change of wind speed). The state transition matrix is ​​modeled based on the dynamic characteristics of wind speed, typically using a continuous-time model with discretization. The sampling period is set between 0.1 and 1 second to match the real-time requirements of the wind turbine control system. The observation model models the linear relationship between the sensor voltage signal and the actual wind speed as an observation equation, which includes the sensor gain coefficient and offset, and needs to be determined through calibration experiments.

[0032] Furthermore, the noise parameter settings of the Kalman filter are crucial. The process noise covariance matrix Q is used to describe the uncertainty of the system model, and is usually set as a diagonal matrix based on the statistical characteristics of wind speed changes, with the main diagonal elements ranging from 0.01 to 0.1 m. 2 / s 4 The measurement noise covariance matrix R reflects the sensor's measurement error. The R values ​​for ultrasonic and mechanical anemometers are set to 0.05–0.2 m, respectively. 2 / s 2 This is to reflect the differences in measurement accuracy under different environments.

[0033] The filtering frequency range for this step is set to 0.1-20Hz to preserve the low-frequency trend and mid-frequency fluctuations in the wind speed signal while filtering out high-frequency noise and vibration interference. By setting appropriate filter parameters, smooth processing of the wind speed signal can be achieved, providing high-quality input for subsequent calculations of instantaneous values, short-term averages, and long-term averages. This enhances the unit's early warning and cut-off response capabilities under extreme wind conditions, significantly improving the operational safety and control reliability of the wind turbine.

[0034] S2, calculate the instantaneous average, short-term average and long-term average of the current wind speed based on the nacelle wind speed signal, and at the same time convert the lidar wind speed signal into wind speed data that matches the distance and height of the wind turbine, and calculate the instantaneous average, short-term average and long-term average of the lidar wind speed.

[0035] Specifically, this step involves the processing and analysis of multi-source wind speed signals in the wind turbine protection and control system. This includes calculating the instantaneous average, short-term average, and long-term average of the nacelle wind speed signal and the lidar wind speed signal, respectively. In some implementations, the nacelle wind speed signal is collected by a mechanical or ultrasonic anemometer mounted on the nacelle. The raw data is usually an analog voltage signal or a digital pulse signal, which needs to be preprocessed by a signal conditioning circuit and an analog-to-digital converter module to finally convert it into a wind speed value in m / s. The instantaneous average usually refers to the arithmetic mean of the wind speed within the current sampling period. The sampling frequency is generally set to 10Hz to 50Hz to capture rapid changes in wind speed. The short-term average can be calculated using a sliding time window, such as the average wind speed over 10 or 30 seconds, to identify short-term fluctuations in wind speed. The long-term average is usually the average over a window of 10 minutes or longer, conforming to the requirements for wind speed measurement in the IEC 61400-25 standard, and is used to assess the continuous changes in wind conditions.

[0036] For lidar wind speed signals, the raw data is wind speed vector information measured through the Doppler effect. This data needs to undergo coordinate transformation and range-height matching to map the wind speed data from the radar detection point to the center of the wind turbine. In some implementations, interpolation algorithms (such as linear interpolation or cubic spline interpolation) are used to spatially reconstruct wind speed data at different detection distances and heights, ensuring that the wind speed data is consistent with the actual operating environment of the wind turbine. Subsequently, the instantaneous, short-time, and long-time average values ​​of the lidar wind speed are calculated to achieve synchronous comparison and fusion analysis with the nacelle wind speed signal.

[0037] This step plays a crucial role in the entire protection and control method. By calculating the average wind speed over multiple time scales, it can more accurately identify the approaching trend of extreme wind conditions, providing reliable data support for subsequent high wind warnings and protection switching logic. Its technical value lies in improving the response speed and judgment accuracy of wind turbines in complex wind field environments, thereby effectively reducing the dynamic load on key components and enhancing the operational safety and survivability of the unit under extreme weather conditions such as typhoons and hurricanes.

[0038] Furthermore, S2 includes: S21, a weighted average algorithm is used to fuse the cabin wind speed signal and the lidar wind speed signal, wherein the weighting coefficient of the lidar wind speed signal is 0.6-0.8.

[0039] Specifically, in this invention, the use of a weighted average algorithm to fuse the nacelle wind speed signal and the lidar wind speed signal is one of the core steps in realizing multi-source wind speed information fusion control of wind turbine units. This step aims to improve the accuracy and timeliness of wind speed measurement by rationally allocating the weights of different sensors, thereby enhancing the protection and control capabilities of the unit under extreme wind conditions.

[0040] Furthermore, the weighted average algorithm sets the weighting coefficient of the lidar wind speed signal to 0.6 to 0.8 to reflect its superior performance in wind speed prediction. Lidar has advantages such as non-contact measurement, high spatial resolution, and fast time response, and can acquire undisturbed raw wind speed information in front of the wind turbine, making it particularly suitable for high wind warnings and feedforward control. While nacelle wind speed signals (such as mechanical or ultrasonic anemometers) measure the wind speed at the current location of the turbine, they are easily affected by factors such as turbine wake, icing, and vibration, resulting in a certain degree of lag and distortion.

[0041] Furthermore, the signal processing unit first performs synchronous sampling and preprocessing of the cabin wind speed signal and the lidar wind speed signal, including filtering, noise reduction, and time alignment, to eliminate timing deviations and abnormal fluctuations between the signals.

[0042] Furthermore, this fusion algorithm is widely used in standby state early warning and cut-out protection control of wind turbines in grid-connected operation. For example, in standby mode, the fused wind speed signal is used to determine whether the wind speed threshold prohibiting startup (typically 25 m / s to 30 m / s) is exceeded, thereby deciding whether to allow the turbine to start. In grid-connected operation, the fused signal is used to determine the instantaneous, short-term (e.g., 10-minute average), and long-term (e.g., 60-minute average) cut-out wind speed thresholds in real time, ensuring that the turbine shuts down in time under extreme wind conditions to avoid overload damage.

[0043] This step yields significant technical benefits, not only improving the robustness and reliability of wind speed measurements but also effectively reducing the risk of misjudgments due to single sensor failure or measurement deviation. By introducing forward-looking wind speed information from lidar, the system can respond protectively several seconds to tens of seconds in advance, thereby significantly reducing the dynamic load on critical components such as blades, main shafts, and towers, and enhancing the unit's survivability and operational safety under extreme weather conditions such as typhoons and hurricanes.

[0044] S22, a wind speed propagation time model is established based on the wind turbine rotation radius and lidar detection distance. The lidar wind speed signal is dynamically compensated according to the propagation time and then the average value is calculated.

[0045] Specifically, this step involves establishing a wind speed propagation time model based on the wind turbine's rotation radius and the lidar detection distance, and calculating the average value after dynamically compensating the lidar wind speed signal. Its core lies in accurately modeling the propagation delay of the wind speed signal in space to achieve real-time and accurate perception of the undisturbed wind field ahead, thereby improving the protection and control response capability of wind turbines under extreme wind conditions.

[0046] Furthermore, this step first obtains the rotation radius of the wind turbine. (Typically half the diameter of the wind turbine, for example, the radius of a 100-meter wind turbine is 50 meters), and the detection range of the lidar. (For example, the detection range of a forward-looking lidar is 200 to 500 meters). Based on the wind turbine's rotation radius and the distance between the lidar detection point and the wind turbine, calculate the time required for the wind speed signal to travel from the detection point to the center of the wind turbine. ,in This is the current wind speed estimate. The propagation time model can be further integrated with wind direction information, employing a vector decomposition method to consider only the wind speed component along the wind turbine axis, thereby improving the accuracy of time compensation.

[0047] Furthermore, the propagation time model calculation needs to consider the sampling frequency of the wind speed signal (e.g., 10Hz or higher), as well as the scanning period and spatial resolution of the lidar. The compensation algorithm can employ interpolation or sliding window filtering techniques to perform time alignment processing on the wind speed signal, ensuring that the lidar data is consistent with the actual wind exposure time of the wind turbine. The compensated wind speed signal will be used to calculate the instantaneous average, short-term average (e.g., 10-second moving average), and long-term average (e.g., 10-minute moving average) to meet the needs of different protection logics.

[0048] Furthermore, this step is mainly used for high wind warning and cut-off protection control of wind turbine units in standby and grid-connected operation. By dynamically compensating for the propagation delay of lidar wind speed signals, the system can predict the wind conditions that the wind rotor will face several seconds to tens of seconds in advance, thereby taking protective measures before extreme wind speeds arrive, such as prohibiting startup or stopping the turbine in advance by feathering.

[0049] Furthermore, this step effectively solves the measurement deviation problem caused by position lag and flow field distortion in traditional anemometers, improves the foresight and accuracy of wind speed signals, provides a reliable basis for the protection and control of wind turbine units, significantly reduces the dynamic load and equipment damage risk under extreme wind conditions, and enhances the operational safety and stability of the units in harsh environments such as typhoons and hurricanes.

[0050] S3, when the unit is in standby mode, if the long-term average value of the nacelle wind speed signal or the long-term average value of the lidar wind speed signal exceeds the preset wind speed threshold for prohibiting startup, the unit is prohibited from starting and remains in standby mode.

[0051] Specifically, when the unit is in standby mode, if the long-term average value of the nacelle wind speed signal or the long-term average value of the lidar wind speed signal exceeds the preset wind speed threshold for prohibiting startup, the unit is prohibited from starting and remains in standby mode. This step is one of the core logics of the wind turbine high wind warning and protection control method in this invention, which aims to improve the operational safety and reliability of the unit under extreme wind conditions.

[0052] Further, this step involves acquiring nacelle wind speed signals and lidar wind speed signals through a data acquisition unit, and then calculating their long-term average values ​​using a signal processing unit. The long-term average value is typically defined as a moving average of wind speed data over a set time window (e.g., 10 minutes) to reflect the continuous trend of wind conditions. When the unit is in standby mode (e.g., shut down for startup, under maintenance, or immediately after shutdown), the control system monitors the long-term average values ​​of these two wind speed signals in real time. If the long-term average value of either signal exceeds a preset wind speed threshold prohibiting startup (e.g., 25 m / s or as set according to unit design standards), the control system will trigger a protection mechanism to prevent the unit from entering the startup process, ensuring that the unit is not activated before extreme wind conditions arrive, thereby avoiding mechanical shock and structural damage caused by sudden wind speed changes.

[0053] Furthermore, the threshold for prohibiting turbine startup wind speed needs to be calibrated based on the structural strength of the wind turbine, design specifications (such as IEC 61400-23 and IEC 61400-24), and historical extreme wind condition data. The calculation time window for long-term averages is typically 10 minutes, with a sampling frequency of 1 Hz to 10 Hz, which can be optimized based on the stability of the wind speed signal and the system response speed. In addition, the lidar wind speed signal needs to undergo coordinate transformation and rotor position matching processing to ensure its spatial consistency with the nacelle wind speed signal.

[0054] Furthermore, this procedure is applicable to the standby control of wind turbines under extreme weather conditions such as typhoons and hurricanes. In coastal or high-wind-speed areas, turbines may face structural overload risks due to sudden increases in wind speed. By utilizing the forward-looking wind measurement capabilities of lidar, the system can identify wind speed trends before the rotor is affected by wind, thereby achieving more precise start-up control.

[0055] Furthermore, this step effectively avoids the false start-up problem caused by response lag and flow field distortion in traditional anemometers, significantly improving the unit's survivability under extreme wind conditions. By integrating lidar and nacelle wind speed signals, the system possesses stronger environmental awareness, providing a solid foundation for subsequent cut-out protection and dynamic load control, demonstrating significant engineering practical value and innovation.

[0056] Furthermore, S3 includes: S31, when the long-term average value of the lidar wind speed signal exceeds the prohibition threshold for starting, the yaw system is activated to adjust the orientation of the unit to avoid extreme wind directions.

[0057] Specifically, when the long-term average value of the lidar wind speed signal exceeds the threshold prohibiting startup, the yaw system is activated to adjust the turbine's orientation to avoid extreme wind directions. This is one of the key execution steps in the wind turbine high wind warning and protection control method of this invention. This step, based on the forward-looking wind measurement capability of the lidar, enables forward-looking wind condition judgment and active wind avoidance control of the wind turbine in standby mode.

[0058] Furthermore, the lidar emits multiple laser beams in front of the wind turbine, utilizing the Doppler effect to measure the velocity of aerosol particles in the atmosphere, thereby acquiring wind speed and direction information. The signal processing unit filters, transforms, and synthesizes the raw data collected by the lidar, ultimately outputting a wind speed signal that matches the center position and height of the wind turbine. This signal is then processed by a 10-minute moving average to obtain the long-term average value of the lidar wind speed signal. When this long-term average value exceeds the set wind speed threshold prohibiting startup (e.g., 25 m / s, according to the definition of extreme wind speed in IEC 61400-23), the control system will trigger the active adjustment mechanism of the yaw system.

[0059] Furthermore, the wind speed threshold prohibiting turbine startup can be dynamically configured based on the structural strength of the wind turbine, its design limit wind speed (such as the 50-year return period extreme wind speed specified in IEC 61400-1), and the operating environment. The yaw system response time is typically controlled within 30 seconds to ensure that the turbine's orientation is adjusted before extreme wind conditions arrive. The yaw angle adjustment range is generally ±90°, with a step angle of 5°, to gradually avoid areas of sudden wind direction changes.

[0060] Furthermore, this step applies to the standby state of wind turbines under extreme weather conditions such as typhoons and hurricanes. By adjusting the turbine's orientation in advance, the frontal windward area of ​​the rotor can be effectively reduced, lowering the dynamic load on the blades, main shaft, and tower, thereby improving the structural safety and operational stability of the turbine under extreme wind conditions.

[0061] Furthermore, this step enables proactive wind avoidance control of wind turbines before extreme wind conditions arrive, avoiding the risk of false starts or immediate encounter with extreme wind loads after start-up due to the lag in response of traditional anemometers, and significantly improving the survivability and operational reliability of the units.

[0062] S32, if the long-term average difference between the cabin wind speed signal and the lidar wind speed signal exceeds 1.5 m / s, the sensor anomaly detection process is triggered and the lidar signal is used first for protection judgment.

[0063] Specifically, in some implementations, when the long-term average difference between the nacelle wind speed signal and the lidar wind speed signal exceeds 1.5 m / s, the system will trigger a sensor anomaly detection process and prioritize the lidar signal for protection judgment. This step, based on the fusion and difference analysis of multi-source wind speed signals, aims to improve the accuracy and reliability of the wind turbine's protection response under extreme wind conditions.

[0064] Furthermore, the signal processing unit continuously receives wind speed data from the nacelle anemometer (such as an ultrasonic or mechanical anemometer) and the forward-looking scanning lidar. The system filters, calibrates, and synchronizes the two types of signals separately to eliminate data deviations caused by sensor installation location, response characteristics, or environmental interference (such as icing or vibration). Subsequently, the system calculates the long-term average of the two signals, typically using a moving average algorithm with a 10-minute time window to reflect the stable trend of wind conditions. When the difference between the two exceeds 1.5 m / s, the system determines that there is a sensor malfunction or wind field distortion, thereby initiating an anomaly detection process, evaluating the effectiveness of the nacelle wind speed signal, and automatically switching to the lidar signal as the basis for protection decisions after confirming the anomaly.

[0065] Furthermore, the 1.5 m / s threshold is based on the tolerance requirements for wind speed measurement errors in the IEC 61400-25 standard, and is set in conjunction with the safety boundary of wind turbines under extreme wind conditions such as typhoons. The sampling frequency of lidar signals is typically 10 Hz to 20 Hz, with a measurement distance exceeding 500 meters and a spatial resolution between 10 and 50 meters. It can provide raw wind condition data undisturbed by the wind turbine, significantly outperforming the measurement accuracy and response speed of traditional anemometers.

[0066] Furthermore, this step is mainly applied to the high-wind cut-out protection control of wind turbine units in grid-connected operation. When the system detects an abnormal increase in incoming wind speed, it can trigger shutdown protection in advance based on lidar signals, and send a feathering command to the pitch system to achieve a smooth transition of the unit from grid-connected state to shutdown state, thereby effectively reducing the dynamic load on the blades, main shaft and tower, and improving the unit's survivability under extreme weather conditions.

[0067] Furthermore, the technical effect of this step is that by introducing lidar signals as the basis for priority protection, it overcomes the problems of misjudgment or missed judgment caused by flow field distortion and response lag in traditional anemometers, enhances the robustness and foresight of the protection system, and provides key guarantees for the safe operation of wind turbines under complex wind conditions.

[0068] S4. When the unit is in grid-connected operation, based on the combined judgment results of the instantaneous average value, short-term average value and long-term average value of the nacelle wind speed signal and the lidar wind speed signal, when the average wind speed in any time window exceeds the corresponding cut-out wind speed threshold, a feathering command is sent to the pitch system and the unit is controlled to stop and cut out.

[0069] Specifically, in the grid-connected operation state of the wind turbine, based on the combined results of the instantaneous average, short-term average, and long-term average values ​​of the nacelle wind speed signal and the lidar wind speed signal, when the average wind speed in any time window exceeds the corresponding cut-out wind speed threshold, a feathering command is issued to the pitch system to control the turbine to shut down and cut out. This is one of the core steps in realizing the high-wind cut-out protection control of wind turbines in this invention. This step improves the accuracy and timeliness of wind speed measurement by fusing multi-source wind speed data, thereby optimizing the protection response mechanism of the turbine under extreme wind conditions.

[0070] Furthermore, this step first relies on the signal processing unit to process the nacelle wind speed signal (acquired by an ultrasonic or mechanical anemometer) and the lidar wind speed signal in real time. Instantaneous averages typically employ a sliding window algorithm with a time window length of 1 to 3 seconds to capture rapid changes in wind speed; short-term averages use a sliding window of 5 to 10 seconds to reflect short-term wind speed trends; and long-term averages are based on statistical averages of 10 to 30 minutes to assess the persistence of wind conditions. The lidar wind speed signal needs to undergo coordinate transformation and rotor position matching processing to ensure its spatial consistency with the nacelle wind speed signal.

[0071] Furthermore, the instantaneous cut-out wind speed threshold is generally set to 25 m / s to 30 m / s, the short-term cut-out wind speed threshold is 22 m / s to 28 m / s, and the long-term cut-out wind speed threshold is 20 m / s to 25 m / s. The specific values ​​are adjusted according to the unit design specifications (such as IEC 61400-23) and the wind farm environmental conditions. When the average wind speed in any time window exceeds the corresponding threshold, the control system will trigger the protection logic and send a feathering command to the pitch system, so that the blade angle is gradually adjusted to the feathering position, thereby reducing the wind turbine's wind-catching capacity and achieving a smooth unit shutdown.

[0072] Furthermore, this procedure is applicable to the operational protection of wind turbines under extreme weather conditions such as typhoons and hurricanes. Utilizing the forward-looking wind measurement capability of lidar, the system can respond before the wind turbine is impacted by extreme wind speeds, avoiding the overload risk caused by the lag in response of traditional anemometers. In addition, this method can be integrated with SCADA systems to achieve remote monitoring and automatic protection control.

[0073] Furthermore, this step effectively improves the unit's response speed and control accuracy under extreme wind conditions, significantly reduces the dynamic load and fatigue damage of key components such as blades, main shaft, and tower, and enhances the unit's operational safety and survivability, demonstrating significant engineering practical value and innovation.

[0074] Furthermore, S4 includes: S41, when the instantaneous average value exceeds the cut-out wind speed threshold, the response delay time for issuing a feathering command to the pitch system is less than 0.5 seconds.

[0075] Specifically, when the instantaneous average value exceeds the cut-out wind speed threshold, the control system needs to issue a feathering command to the pitch system to achieve rapid shutdown protection of the wind turbine. The key to this step is the control of the response delay time, which is required to be less than 0.5 seconds, to ensure that the unit can leave the high wind speed area in time under extreme wind conditions, avoiding mechanical overload and structural damage caused by sudden changes in wind speed.

[0076] Furthermore, this response mechanism relies on a high-real-time data acquisition and processing system. The lidar acquires raw wind speed data of the wind field in front of the wind turbine using a forward-looking scanning method with a sampling frequency of 10Hz to 20Hz. The signal processing unit then performs filtering, interpolation, and coordinate transformation to generate a wind speed signal that matches the wind turbine's position and height. The instantaneous average value is typically calculated using a sliding window method with a window length of 1 to 3 seconds to balance real-time performance and data stability. When this instantaneous average value exceeds a set cut-out wind speed threshold (e.g., 25 m / s or higher, according to unit design standards IEC 61400-23 or GB / T19963), the protection unit immediately triggers shutdown logic.

[0077] Furthermore, the response delay time must meet the requirements of IEC 61400-25 regarding control system response time. The communication protocol between the control system and the pitch system typically uses high-speed Ethernet or CAN bus, with the data transmission cycle controlled within 10ms to ensure real-time command issuance. The execution time of the feathering command is determined by the response speed of the pitch system's servo motor, generally between 0.3 and 0.4 seconds. Therefore, the delay of the entire control link must be controlled within 0.5 seconds to achieve fast and smooth blade angle adjustment.

[0078] Furthermore, this step is primarily used for emergency shutdown protection when wind turbines encounter sudden extreme wind conditions (such as typhoons or gusts) while operating in grid-connected mode. Utilizing the forward-looking wind measurement capabilities of lidar, the system can respond before the rotor is impacted, effectively reducing main shaft torque, blade load, and tower vibration, thereby enhancing the unit's survivability in harsh environments.

[0079] Furthermore, this step significantly improves the response speed and protection accuracy of wind turbine units under extreme wind conditions, effectively avoids the "too late cut-out" problem caused by the response lag of traditional anemometers, reduces fatigue damage to key components of the unit, and extends equipment life. It has important engineering application value and innovative significance.

[0080] S42 dynamically adjusts the rate of change of the feathering angle based on the duration of the wind speed signal exceeding the threshold; the longer the duration, the lower the rate of change of the angle.

[0081] Specifically, the step in this invention of "dynamically adjusting the rate of change of the feathering angle based on the duration of the wind speed signal exceeding a threshold, with a lower rate of change for a longer duration" is one of the key control strategies for wind turbines to achieve smooth cut-out and reduce dynamic loads under extreme wind conditions. This step is based on the fusion analysis of multi-source wind speed signals (including nacelle anemometer and lidar wind speed signals), combined with the length of time the wind speed exceeds the set cut-out threshold, to dynamically adjust the feathering rate of the pitch system, thereby achieving smooth control of the aerodynamic load on the wind turbine.

[0082] Furthermore, this step involves a time window analysis of the wind speed signal by a signal processing unit to calculate the duration during which the wind speed exceeds the cut-out threshold (e.g., 25 m / s). Based on this duration, the control system dynamically adjusts the feathering angle change rate using a preset nonlinear function or a lookup table method. For example, if the duration of the wind speed exceeding the threshold is less than a set short window (e.g., 10 seconds), the feathering angle change rate can be set to a higher value (e.g., 5° / s) to quickly respond to sudden strong winds and prevent overspeeding; conversely, if the duration exceeds a set long window (e.g., 60 seconds), the feathering angle change rate gradually decreases to a lower value (e.g., 1° / s) to mitigate the impact load caused by blade angle changes and avoid fatigue damage to the main shaft, gearbox, and tower.

[0083] Furthermore, this control strategy can combine the instantaneous value, short-term average value (such as a 10-second moving average), and long-term average value (such as a 60-second moving average) of the wind speed signal for comprehensive judgment, ensuring optimal feathering control under different wind conditions. In practical applications, this step can be deployed in the PLC or DCS control system of the wind turbine, working in conjunction with the pitch controller, and is suitable for protection and control scenarios under extreme weather conditions such as typhoons and hurricanes.

[0084] Furthermore, by introducing a time dimension to dynamically adjust the feathering rate, the contradiction between the unit's response speed and structural safety is effectively balanced, significantly improving the wind turbine's survivability and operational reliability under extreme wind conditions.

[0085] This invention discloses a wind turbine protection and control method equipped with lidar. By adding lidar, wind speed can be measured in advance. The protection and control logic is optimized by combining multi-source wind speed information. This effectively reduces the dynamic load on key components of the turbine under extreme wind conditions, improves the turbine's operational safety and survivability, and avoids the risk of late cut-off due to delayed wind speed measurement.

[0086] Example 2 This invention proposes a wind turbine protection and control system equipped with lidar, overcoming the aforementioned deficiencies of existing technologies. It provides a multi-source wind speed information fusion solution, integrating lidar data from the nacelle, ultrasonic anemometer, and mechanical anemometer, thereby optimizing the protection functions of the wind turbine. This invention can improve the operational safety and reliability of the turbine under extreme wind conditions, reduce the dynamic load on key components (blades, main shaft, tower), and extend their service life.

[0087] In one embodiment of the present invention, a wind turbine protection control system equipped with lidar mainly includes: Specifically, the data acquisition unit includes an ultrasonic anemometer, a mechanical anemometer, and a forward-looking lidar mounted on the top of the cabin. The signal processing unit receives and processes the raw data from all the data acquisition units.

[0088] Specifically, this includes converting the voltage signals uploaded by the mechanical anemometer and ultrasonic anemometer into corresponding mechanical anemometer wind speed signals. and the wind speed signal of the ultrasonic anemometer Simultaneously, the current wind speed signal is calculated based on a combination of ultrasonic and mechanical wind speed signals. Display the current wind speed signal. The calculation can use the average value or the root mean square value.

[0089]

[0090] or

[0091] And using the current wind speed signal Calculate the instantaneous value of the current wind speed signal Short-term average and long-term average Simultaneously, the communication data uploaded by the lidar is converted into wind speed signals that match the distance and height of the wind turbine rotor. And calculate the instantaneous average value of the lidar wind speed signal. Short-term average and long-term average .

[0092] Furthermore, the protection unit: based on the wind speed signal obtained from the signal processing unit, when the unit is in different states, it implements and optimizes protection functions such as high wind warning and high wind cut-off, and issues protection control commands to each actuator.

[0093] Furthermore, in the standby state of the unit, if the wind speed signal is detected before extreme wind conditions arrive... Or lidar wind speed signal Exceeding the set threshold for the fan speed that prohibits starting At this time, the unit is prohibited from starting and will not enter the starting state; Furthermore, when the unit is in grid-connected operation, if the wind speed signal or lidar wind speed exceeds the unit's designed cut-off wind speed, the unit will shut down and cut off.

[0094] In one embodiment of the present invention, such as Figure 2 As shown, a wind turbine generator set equipped with lidar is proposed for high wind early warning and protection control.

[0095] Specifically, in standby mode, the average value of the current nacelle wind speed signal is continuously monitored based on the existing nacelle anemometer. And the long-term average value of the radar wind speed signal continuously monitored by lidar. .

[0096] Furthermore, determine whether the incoming air velocity exceeds the unit's designed prohibition start-up air velocity. That is, when the average value of the cabin wind speed signal Or the average value of radar wind speed signal Exceeding the set threshold for the fan speed that prohibits starting At this time, the unit is prohibited from starting and will not enter the startup state.

[0097] In one embodiment of the present invention, such as Figure 3 As shown, a wind turbine generator set with added lidar is proposed for high wind shedding protection control.

[0098] Specifically, during the grid-connected operation of the unit, if the instantaneous average value of the nacelle wind speed signal... Or the instantaneous average value of the lidar wind speed signal Exceeding the instantaneous cut-out wind speed designed for the unit When this happens, the unit shuts down and switches out, and the control system sends a feathering command to the pitch system, causing the unit to switch from grid-connected status to shutdown status.

[0099] Furthermore, if the short-time average value of the cabin wind speed signal... Or the short-time average value of the lidar wind speed signal Exceeding the unit's design short-time cut-out velocity When this happens, the unit shuts down and switches out, and the control system sends a feathering command to the pitch system, causing the unit to switch from grid-connected status to shutdown status.

[0100] Furthermore, if the long-term average value of the cabin wind speed signal... Or the long-term average value of the lidar wind speed signal Exceeding the unit's design long-term cut-out velocity When this happens, the unit shuts down and switches out, and the control system sends a feathering command to the pitch system, causing the unit to switch from grid-connected status to shutdown status.

[0101] The embodiments of the present invention also have the following technical effects: Based on the above, this invention proposes a protection and control method for wind turbines equipped with lidar. By installing lidar on the wind turbine, it is possible to detect extreme winds that are about to arrive in advance. When the turbine is in standby mode, it can prevent the turbine from starting before the arrival of strong winds; and when the turbine is in grid-connected operation, it can achieve a smooth and orderly shutdown when strong winds arrive, significantly reducing the extreme load on the turbine, completely avoiding the risk of "late cut-off" caused by lag in wind speed measurement, greatly improving the turbine's survivability in extreme weather conditions such as typhoons and hurricanes, fundamentally eliminating catastrophic accidents caused by such weather, and extending the service life of the turbine.

[0102] In one embodiment of the present invention, such as Figure 4 As shown, an alternative solution is: In the control method, the judgment basis of the wind turbine high wind warning protection control method can be based on the actual application and operating environment of the unit, using only the nacelle anemometer signal or lidar data signal, while the judgment basis of the wind turbine high wind cut-out protection control method can use both the nacelle anemometer signal and lidar data signal.

[0103] Example 3 To achieve the above embodiments, such as Figure 5 As shown, this embodiment also provides a wind turbine protection and control device 10 equipped with lidar, including: The synchronous data acquisition module 100 is used to synchronously acquire wind speed data through an ultrasonic anemometer, a mechanical anemometer, and a forward-looking scanning lidar installed on the top of the cabin, and to obtain cabin wind speed signals and lidar wind speed signals. The wind speed data processing module 200 is used to calculate the instantaneous average, short-term average and long-term average of the current wind speed based on the nacelle wind speed signal, and at the same time convert the lidar wind speed signal into wind speed data that matches the distance and height of the wind turbine, and calculate the instantaneous average, short-term average and long-term average of the lidar wind speed. The standby state protection module 300 is used to prevent the unit from starting and maintain the standby state when the long-term average value of the nacelle wind speed signal or the long-term average value of the lidar wind speed signal exceeds a preset wind speed threshold that prohibits starting the unit. The grid-connected operation protection module 400 is used to, in the grid-connected operation state of the unit, based on the combined judgment results of the instantaneous average value, short-term average value and long-term average value of the nacelle wind speed signal and the lidar wind speed signal, issue a feathering command to the pitch system and control the unit to stop and cut off when the average wind speed in any time window exceeds the corresponding cut-out wind speed threshold.

[0104] Furthermore, the synchronous data acquisition module 100 is also used for: The three-dimensional wind speed vector of the wind field ahead is calculated by using the Doppler frequency shift of lidar, and vertical wind shear correction is performed by combining the wind turbine height distribution. The Kalman filter algorithm is used to perform real-time noise reduction on the voltage signals of ultrasonic anemometers and mechanical anemometers, with a filtering frequency range of 0.1-20Hz.

[0105] Furthermore, the wind speed data processing module 200 is also used for: A weighted average algorithm is used to fuse the cabin wind speed signal and the lidar wind speed signal, wherein the weighting coefficient of the lidar wind speed signal is 0.6-0.8; A wind speed propagation time model is established based on the wind turbine rotation radius and lidar detection distance. The lidar wind speed signal is dynamically compensated according to the propagation time and then the average value is calculated.

[0106] Furthermore, the standby state protection module 300 is also used for: When the long-term average value of the lidar wind speed signal exceeds the prohibition threshold for starting, the yaw system is activated to adjust the unit's orientation to avoid extreme wind directions. If the long-term average difference between the cabin wind speed signal and the lidar wind speed signal exceeds 1.5 m / s, the sensor anomaly detection process is triggered, and the lidar signal is used first for protection judgment.

[0107] Furthermore, the grid-connected operation protection module 400 is also used for: When the instantaneous average value exceeds the cut-out wind speed threshold, the response delay time for issuing a feathering command to the pitch system is less than 0.5 seconds; The rate of change of the feathering angle is dynamically adjusted based on the duration for which the wind speed signal exceeds the threshold; the longer the duration, the lower the rate of change of the angle.

[0108] The method and apparatus of this invention, by adding lidar and fusing data from traditional wind speed sensors, enable wind turbines to achieve proactive early warning and graded protection control under extreme wind conditions, effectively reducing the dynamic load of the unit and the operational risks under extreme weather, and improving system safety and equipment lifespan.

[0109] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0110] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

Claims

1. A method of retrofitting a wind turbine with a lidar-based protection control, characterized in that, The method comprises the following steps: S1, acquiring a nacelle wind speed signal and a laser radar wind speed signal by synchronously collecting wind speed data through an ultrasonic anemometer, a mechanical anemometer and a forward-looking scanning laser radar installed on the top of the nacelle; S2, calculating the instantaneous average value, short-time average value and long-time average value of the current wind speed based on the nacelle wind speed signal, and converting the laser radar wind speed signal into wind speed data matched with the distance and height of the wind wheel, and calculating the instantaneous average value, short-time average value and long-time average value of the laser radar wind speed; S3, in the standby state of the unit, when the long-time average value of the nacelle wind speed signal or the long-time average value of the laser radar wind speed signal exceeds a preset prohibited start-up wind speed threshold value, the unit is prohibited to start and remains in the standby state; S4, in the grid-connected operation state of the unit, according to the combination judgment result of the instantaneous average value, short-time average value and long-time average value of the nacelle wind speed signal and the laser radar wind speed signal, when the average value of the wind speed in any time window exceeds the corresponding cut-out wind speed threshold value, an order of feathering is sent to the variable pitch system and the unit is controlled to stop and cut out.

2. The method of claim 1, wherein, The S1 further comprises: S11, calculating the three-dimensional wind speed vector of the front wind field through the Doppler frequency shift of the laser radar, and combining the height distribution of the wind wheel to perform vertical wind shear correction; S12, performing real-time denoising processing on the voltage signals of the ultrasonic anemometer and the mechanical anemometer by using a Kalman filtering algorithm, and the filtering frequency range is 0.1-20Hz.

3. The method of claim 1, wherein, The S2 further comprises: S21, performing fusion processing on the nacelle wind speed signal and the laser radar wind speed signal by using a weighted average algorithm, wherein the weight coefficient of the laser radar wind speed signal is 0.6-0.8; S22, establishing a wind speed propagation time model based on the rotating radius of the wind wheel and the detection distance of the laser radar, and calculating the average value after dynamically compensating the laser radar wind speed signal according to the propagation time.

4. The method of claim 1, wherein, The S3 further comprises: S31, when the long-time average value of the laser radar wind speed signal exceeds the prohibited start-up threshold value, starting a yaw system to adjust the orientation of the unit to avoid the extreme wind direction; S32, if the difference between the long-time average values of the nacelle wind speed signal and the laser radar wind speed signal exceeds 1.5m / s, triggering a sensor abnormality detection process and preferentially using the laser radar signal for protection judgment.

5. The method of claim 1, wherein, The S4 further comprises: S41, when the instantaneous average value exceeds the cut-out wind speed threshold value, the response delay time of sending the order of feathering to the variable pitch system is less than 0.5s; S42, dynamically adjusting the feathering angle change rate according to the duration time of the wind speed signal exceeding the threshold value, and the longer the duration time is, the lower the angle change rate is.

6. A wind turbine generator protection control device retrofitted with a laser radar, characterized by, The method comprises the following steps: a synchronous data collection module, configured to acquire a nacelle wind speed signal and a laser radar wind speed signal by synchronously collecting wind speed data through an ultrasonic anemometer, a mechanical anemometer and a forward-looking scanning laser radar installed on the top of the nacelle; a wind speed data processing module, configured to calculate the instantaneous average value, short-time average value and long-time average value of the current wind speed based on the nacelle wind speed signal, and convert the laser radar wind speed signal into wind speed data matched with the distance and height of the wind wheel, and calculate the instantaneous average value, short-time average value and long-time average value of the laser radar wind speed; The standby state protection module is configured to, in a standby state of the unit, prohibit the unit from starting and keep the standby state when a long-time average of the cabin wind speed signal or a long-time average of the laser radar wind speed signal exceeds a preset prohibited start wind speed threshold. The grid-connected operation protection module is configured to, in a grid-connected operation state of the unit, according to a combination judgment result of instantaneous average, short-time average and long-time average of the cabin wind speed signal and the laser radar wind speed signal, when the average wind speed of any time window exceeds a corresponding cut-out wind speed threshold, issue a feathering instruction to a variable pitch system and control the unit to stop and cut out.

7. The apparatus of claim 6, wherein, The synchronous data acquisition module is further configured to: Calculate a three-dimensional wind speed vector of a front wind field through Doppler frequency shift of the laser radar, and combine wind wheel height distribution to perform vertical wind shear correction; Use a Kalman filtering algorithm to perform real-time denoising processing on voltage signals of the ultrasonic anemometer and the mechanical anemometer, and the filtering frequency range is 0.1-20 Hz.

8. The apparatus of claim 6, wherein, The wind speed data processing module is further configured to: Use a weighted average algorithm to perform fusion processing on the cabin wind speed signal and the laser radar wind speed signal, and the weight coefficient of the laser radar wind speed signal is 0.6-0.8; Based on a wind wheel rotation radius and a laser radar detection distance, a wind speed propagation time model is established, and the laser radar wind speed signal is dynamically compensated according to the propagation time and then the average value is calculated.

9. The apparatus of claim 6, wherein, The standby state protection module is further configured to: When the long-time average of the laser radar wind speed signal exceeds the prohibited start threshold, start a yaw system to adjust the orientation of the unit to avoid an extreme wind direction; If the long-time average difference between the cabin wind speed signal and the laser radar wind speed signal exceeds 1.5 m / s, a sensor abnormality detection process is triggered, and the laser radar signal is preferentially used for protection judgment.

10. The apparatus of claim 6, wherein, The grid-connected operation protection module is further configured to: When the instantaneous average exceeds the cut-out wind speed threshold, the response delay time of the feathering instruction issued to the variable pitch system is less than 0.5 seconds; According to the duration of the wind speed signal exceeding the threshold, the change rate of the feathering angle is dynamically adjusted, and the longer the duration, the lower the change rate.

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