Fan control method, device and equipment based on laser radar and medium

Abstract

The embodiment of the invention discloses a fan control method, device and equipment based on laser radar and a medium. The method comprises the following steps: acquiring radar data corresponding to a plurality of distance sections of a laser radar; fan variable pitch data and fan yaw data are determined according to the radar data; and performing variable pitch control and yaw control on the fan of the laser radar based on the fan variable pitch data and the fan yaw data. According to the technical scheme, the fan variable pitch data and the fan yaw data influencing fan control are determined by using the radar data field output by the laser radar, so that the fan is controlled in an auxiliary manner, and the data efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of wind power generation, and in particular to a laser radar-based wind turbine control method, device, equipment and medium. Background Art

[0002] In recent years, with the development of lidar technology, lidar has been increasingly used onshore and offshore in the wind energy industry, mainly to measure wind speed and direction to help optimize the layout and operation of wind farms.

[0003] Lidar is a laser wind-measuring radar installed on the top of the wind turbine cabin. It can obtain wind information at a distance in front of the wind turbine impeller and can be applied to wind turbine control, power and load test verification, etc. At present, the main subsystems of the wind turbine control system that Lidar participates in are the pitch control system and the yaw control system. During the application of Lidar, it will be affected by blade occlusion, fog interference, and low aerosol concentration, resulting in reduced data efficiency. How to fully utilize the data fields output by Lidar to participate in wind turbine control still has certain problems. Summary of the invention

[0004] The present invention provides a laser radar-based wind turbine control method, device, equipment and medium, which uses the radar data field output by the laser radar to determine the wind turbine pitch data and wind turbine yaw data that affect the wind turbine control, so as to assist in the wind turbine control and improve the efficiency of the data.

[0005] According to one aspect of the present invention, a laser radar-based wind turbine control method is provided, comprising:

[0006] Obtain radar data corresponding to multiple range sections of the laser radar;

[0007] Determine wind turbine pitch data and wind turbine yaw data according to the radar data;

[0008] The wind turbine of the laser radar is subjected to pitch control and yaw control based on the wind turbine pitch data and the wind turbine yaw data.

[0009] According to another aspect of the present invention, a laser radar-based wind turbine control device is provided, comprising:

[0010] A radar data acquisition module, used to acquire radar data corresponding to multiple range sections of the laser radar;

[0011] A wind turbine data determination module, used to determine wind turbine pitch data and wind turbine yaw data according to the radar data;

[0012] A wind turbine control module is used to perform pitch control and yaw control on the wind turbine of the laser radar based on the wind turbine pitch data and the wind turbine yaw data.

[0013] According to another aspect of the present invention, there is provided an electronic device, the electronic device comprising:

[0014] at least one processor; and

[0015] a memory communicatively connected to the at least one processor; wherein,

[0016] The memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the laser radar-based wind turbine control method described in any embodiment of the present invention.

[0017] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the laser radar-based wind turbine control method described in any embodiment of the present invention when executed.

[0018] The technical solution of the embodiment of the present invention obtains radar data corresponding to multiple distance sections of the laser radar; determines wind turbine pitch data and wind turbine yaw data according to the radar data; and performs pitch control and yaw control on the wind turbine of the laser radar based on the wind turbine pitch data and the wind turbine yaw data. This technical solution uses the radar data field output by the laser radar to determine the wind turbine pitch data and wind turbine yaw data that affect the wind turbine control, so as to assist in the control of the wind turbine and improve the efficiency of the data.

[0019] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0021] Figure 1 is a flow chart of a fan control method based on laser radar according to Embodiment 1 of the present invention;

[0022] Figure 2is an example diagram of the installation position of the laser radar provided according to the first embodiment of the present invention;

[0023] Figure 3 is a flow chart of a fan control method based on laser radar according to Embodiment 2 of the present invention;

[0024] Figure 4 This is a schematic diagram of the orientations of four light beams of a laser radar provided according to the second embodiment of the present invention;

[0025] Figure 5 is a schematic structural diagram of a fan control device based on laser radar according to Embodiment 3 of the present invention;

[0026] Figure 6 It is a structural schematic diagram of an electronic device provided according to Embodiment 4 of the present invention. DETAILED DESCRIPTION

[0027] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0028] It should be noted that the terms "first", "second" and "target" in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0029] Embodiment 1

[0030] Figure 1This is a flow chart of a laser radar-based wind turbine control method provided according to the first embodiment of the present invention. This embodiment is applicable to the case of assisting the control of the laser radar wind turbine. The method can be executed by a laser radar-based wind turbine control device. The laser radar-based wind turbine control device can be implemented in the form of hardware and / or software. The laser radar-based wind turbine control device can be configured in an electronic device with data processing capabilities. Figure 1 As shown, the method includes:

[0031] The technical solution of this embodiment can be applied to assist in controlling the fan of the laser radar. The laser radar in this embodiment can be a cabin radar. Existing cabin radars are all installed in fixed positions in the cabin, and there will be no rotation problems. However, if it is installed on the hub, there will be angle and rotation problems. In order to adjust the height of the hub, the specific conditions of the height and wind speed can be determined according to the laser radar output data field, and the fan control of the airport radar can be coordinated as a whole, so that the data affecting the fan control obtained by the laser radar is more accurate, so as to efficiently capture wind energy for the fan.

[0032] S110, obtaining radar data corresponding to multiple range sections of the laser radar.

[0033] Among them, the laser radar can be understood as a laser wind measuring radar installed on the nacelle of the wind turbine generator set, which can be used to measure the wind condition information at a distance in front of the wind turbine impeller. The laser radar can include 4 light beams. Multiple distance sections can be understood as multiple sections within a set distance range in front of the wind turbine. Among them, the setting range can be set according to actual needs. Exemplarily, the setting range can be a range of 50m-750m. In this embodiment, radar data of multiple sections within a range of 50m-750m in front of the wind turbine can be obtained. Radar data can be understood as a data field output by the laser radar based on the data frequency. In this embodiment, the radar data may include data fields output by the laser radar at different time periods. Radar data may specifically include wind speed data and wind direction data. Specifically, different time periods may include second-level data fields of all distance sections and data fields of all distance sections in specified minutes, etc., and the specific specified minutes can be set according to actual needs.

[0034] For example, the installation position of the laser radar in this embodiment is shown in the figure below: Figure 2As shown. The laser radar is installed on the upper part of the wind turbine cabin. Based on the principle of laser Doppler frequency shift, the wind speed and wind direction of multiple sections within the range of 50m-750m in front of the wind turbine can be obtained simultaneously. The data frequency is 4Hz. The acquired data fields include the second-level data statistics of all distance sections and the 10-minute data statistics of all distance sections. Multiple distance sections are represented as 1, 2, 3, 4...n. In this embodiment, the wind speed data and wind direction data corresponding to multiple sections of the laser radar within the set distance range can be obtained.

[0035] In addition, in this embodiment, when obtaining radar data corresponding to multiple distance sections of the laser radar, the radar system status flag is also obtained. The radar system status flag can be represented by 0 and 1. Specifically, in this embodiment, the laser radar system status flag can be obtained, and its system status bit reflects the health status of the laser radar. If the system status bit is 0, the laser radar data does not participate in the fan control; if the system status bit is 1, the laser radar data participates in the fan control. In this embodiment, only when the laser radar system status bit flag is obtained as 1, the influence data of the laser radar participating in the fan control will continue to be determined based on the radar data.

[0036] S120. Determine wind turbine pitch data and wind turbine yaw data according to the radar data.

[0037] Among them, the wind turbine pitch data can be understood as data that affects the wind turbine pitch control system of the wind turbine. In this embodiment, the wind turbine pitch data can include two types of data: feedforward wind speed and extreme wind conditions. Among them, extreme wind conditions can include wind condition data such as extreme gusts, extreme shear and extreme turbulence. The wind turbine yaw data can be understood as data that affects the wind turbine yaw control system of the wind turbine. In this embodiment, the wind turbine yaw data can be yaw angle data for each wind speed interval.

[0038] In this embodiment, different data processing can be performed on the wind speed data and wind direction data contained in the second-level data fields of all distance sections to obtain feedforward wind speed and extreme wind conditions, and the yaw angle data of each wind speed interval can be determined based on the wind speed data and wind direction data contained in the data fields of specified minutes of all distance sections.

[0039] S130, performing pitch control and yaw control on the wind turbine of the laser radar based on the wind turbine pitch data and the wind turbine yaw data.

[0040] In this embodiment, the feedforward wind speed and extreme wind conditions that affect the laser radar wind turbine pitch control can be determined, and the feedforward wind speed and extreme wind conditions can be transmitted to the wind turbine pitch control system, so that the pitch control system can be combined with other different time filtering parameters to achieve the wind turbine pitch control; and the yaw angle data of each wind speed interval that affects the laser radar yaw control can be determined, and the yaw angle data of each wind speed interval can be transmitted to the wind turbine yaw control system, so that the yaw control system can be combined with other different time filtering parameters to achieve the wind turbine yaw control.

[0041] In this embodiment, starting from the health status of the lidar and combining the data fields output by the lidar in real time, the inversion of feedforward wind speed, identification of extreme wind conditions, and calculation of yaw angle are realized, providing a direct and effective data source for the wind turbine's pitch control system and yaw control system, creating concrete conditions for the wind turbine to efficiently capture wind energy.

[0042] The technical solution of the embodiment of the present invention obtains radar data corresponding to multiple distance sections of the laser radar; determines the wind turbine pitch data and wind turbine yaw data according to the radar data; and performs pitch control and yaw control on the wind turbine of the laser radar based on the wind turbine pitch data and wind turbine yaw data. This technical solution uses the radar data field output by the laser radar to determine the wind turbine pitch data and wind turbine yaw data that affect the wind turbine control, so as to assist in the control of the wind turbine and improve the efficiency of the data.

[0043] Embodiment 2

[0044] Figure 3 This is a flow chart of a laser radar-based wind turbine control method provided according to the second embodiment of the present invention. This embodiment is optimized based on the above embodiment. The specific optimization is as follows: radar data includes radar data of the first time period and radar data of the second time period; wind turbine pitch data includes feedforward wind speed and extreme wind conditions; wind turbine pitch data and wind turbine yaw data are determined according to radar data, including: feedforward wind speed and extreme wind conditions are determined according to radar data of the first time period; wherein extreme wind conditions include extreme gusts, extreme shear and extreme turbulence; wind turbine yaw data are determined according to radar data of the second time period. Figure 3 As shown, the method includes:

[0045] S310, obtaining radar data corresponding to multiple range sections of the laser radar.

[0046] The radar data includes radar data for the first time period and radar data for the second time period; the wind turbine pitch data includes feedforward wind speed and extreme wind conditions. The radar data for the first time period may be wind speed data and wind direction data obtained in a second-level time period corresponding to multiple distance sections. The radar data for the second time period may be wind speed data and wind direction data obtained in a set minute time period corresponding to multiple distance sections. The set minute may be a pre-set minute. In this embodiment, the set minute may be 10 minutes, and may also be set according to actual needs, which is not limited in this embodiment.

[0047] In this embodiment, the radar data of the first period includes second-level data statistics of all distance sections, specifically including: radial wind speed RWS, radial wind speed flag RWS_Status, upper plane vertical height Height_high, lower plane vertical height Height_low, wind turbine hub height Height_hub, axis projection wind speed RAWS, axis projection wind speed flag RAWS_Status, hub height wind speed HWS_hub, hub height wind speed flag HWS_hub_Status, upper plane wind speed HWShigh, The upper plane wind speed flag HWShigh_Status, the lower plane wind speed HWSlow, and the lower plane wind speed flag HWSlow_Status, where the flag 0 is invalid and 1 is valid; the radar data of the second time period in this embodiment includes 10-minute data statistics of all distance sections, specifically including the wind speed at hub height HWS_hub, the wind direction at hub height Dir_hub, the wind speed efficiency at hub height HWS_hub_AVI, and the wind direction data efficiency at hub height Dir_hub_AVI, where the value range of the efficiency is 0-100.

[0048] S320. Determine the feedforward wind speed and extreme wind conditions according to the radar data of the first period.

[0049] Among them, extreme wind conditions include extreme gusts, extreme shear and extreme turbulence. The feedforward wind speed can be understood as referring to the wind speed information in the wind power feedforward control. In this embodiment, the measured wind speed output by the laser radar is the wind speed of the cross section at different distances in front of the impeller. When the wind speed is different, the time for the measured wind speed at the same distance to reach the impeller surface will also be different. In the feedforward control, the feedforward wind speed directly used is a fixed lead time, so it is necessary to convert the radar measured wind speed from a fixed distance to a fixed time to obtain the feedforward wind speed. Extreme gusts can be understood as short-term wind phenomena in a turbulent wind field where the wind speed and / or wind direction change dramatically. Extreme shear can be understood as a drastic change in wind speed with height, that is, a wind shear wind condition. In this embodiment, extreme shear can be understood as a wind condition where the wind speeds of the upper and lower planes of the laser radar differ greatly. Extreme turbulence can be understood as a wind phenomenon in which the wind speed and wind direction change rapidly and dramatically.

[0050] In this embodiment, the wind speed for feedforward control can be determined based on the second-level data statistics of all distance sections, and wind phenomena such as extreme gusts, extreme shear, and extreme turbulence under extreme wind conditions can be identified, so as to perform corresponding wind pitch control on the lidar based on different wind phenomena.

[0051] In this embodiment, optionally, the radar data of the first time period includes an axial projection wind speed and an axial projection wind speed status flag; the feedforward wind speed is determined based on the radar data of the first time period, including: determining the overall delay data based on the axial projection wind speed; when the overall delay data and the axial projection wind speed status flag meet the set conditions, determining the impeller equivalent wind speeds of multiple distance sections; and determining the feedforward wind speed based on the impeller equivalent wind speeds of multiple distance sections and a set weighting factor.

[0052] Among them, the axial projection wind speed refers to the axial projection wind speed of the current axis at different distances. The axial projection wind speed state mark can be understood as being used to identify whether the axial projection wind speed state occurs. The axial projection wind speed state mark in this embodiment can be represented by the numbers 1 and 0. If the axial projection wind speed state mark is 1, it means that the axial projection wind speed state occurs; if the axial projection wind speed state mark is 0, it means that the axial projection wind speed state does not occur. The overall delay data can be the delay time of the single distance section determined when determining the feedforward wind speed. In this embodiment, the overall delay data can be determined by different time data. Specifically, the overall delay data can be determined by the arrival time of the wind at different distances to the impeller surface, the filtering delay time, the control delay set by the wind turbine generator model, and the radar scanning delay time. The setting condition can be pre-set. In this embodiment, the setting condition can be the condition that the overall delay of the single distance section is greater than 0 and the axial projection wind speed state mark of the single distance section is 1.

[0053] In this embodiment, the corresponding axial projection wind speed and axial projection wind speed status mark can be obtained for each distance section in the multiple distance sections, and the arrival time of the wind at different distances to the impeller surface is determined according to the axial projection wind speed, and the overall delay time is obtained by integrating the preset filtering delay time, the control delay set by the wind turbine generator model, and the radar scanning delay time.

[0054] Furthermore, the overall delay data is determined according to the axial projection wind speed, including: obtaining the axial distance, filtering delay time and setting delay time corresponding to multiple distance sections; determining the arrival time according to the axial distance and the axial projection wind speed; determining the overall delay data according to the arrival time, filtering delay time and setting delay time.

[0055] The axis distance may be different axis distances corresponding to different distance sections. The filter delay time may be determined according to a wind speed filter function. The set delay time may include a pre-set control delay time and a radar scanning delay time.

[0056] In this embodiment, the second-level data statistics obtained by the laser radar are used as basic data to obtain the axial projection wind speed RAWS and the axial projection wind speed status flag RAWS_Status corresponding to each of the multiple distance sections.

[0057] In this embodiment, the specific method of determining the arrival time based on the axis distance and the axial projection wind speed is that when the measurement distance is not far and the turbulence is not complicated, according to the Taylor turbulence (frozen turbulence) hypothesis, the time for the wind at the distance X to reach the impeller surface can be calculated as:

[0058]

[0059] Among them, X represents the axis distance corresponding to different distance sections, and RAWS represents the axial projection wind speed at the current axis distance X.

[0060] The filtering delay time obtained in this embodiment can be determined by the wind speed filtering function, wherein the wind speed filtering adopts a first-order low-pass filter, and its transfer function expression is:

[0061]

[0062] The natural frequency of wind speed is generally considered to be very low (less than 0.1Hz), and the filter delay is designed to be T filter =τ, the filtering delay time T in this embodiment filter = 15s. In this embodiment, it takes a certain amount of time from when the wind turbine receives the pitch change action command to when the turbine actually executes the action and the wind energy absorption characteristics of the blades change. Different control delays can be set to T according to different wind turbine models. control , can be set according to actual needs. The synthetic wind speed of a plane measured by the laser radar is synthesized from the measurement results of four beams. Therefore, the synthetic wind speed output at the current moment is actually obtained by combining the data within this scanning cycle. The radar scanning delay time can be set to T lida =0.5s.

[0063] In this embodiment, the overall delay data is determined according to the arrival time, the filtering delay time and the setting delay time, that is, the overall delay required for the feedforward calculation is:

[0064] T delay =T tay -T filter -T control -Tlidar .

[0065] In this embodiment, when the overall delay time of a single distance section is greater than 0 and the axial projection wind speed state flag of the single distance section is 1, it can be determined that the impeller equivalent wind speed Vi of the single distance section = the axial projection wind speed of the single distance section, and so on, the impeller equivalent wind speeds of multiple distance sections are determined. In this embodiment, if the overall delay data and the axial projection wind speed state flag do not meet the set conditions, the impeller equivalent wind speed is an invalid value.

[0066] For example, in this embodiment, when the T of the single distance section is satisfied delay >0&&When RAWS_Status of a single distance section is 1, the impeller equivalent wind speed Vi of the single distance section is equal to RAWS of the single distance section, i=1,2,3,4…n; otherwise, the impeller equivalent wind speed Vi is an invalid value.

[0067] The weighted factor may be pre-set, and in this embodiment, may be set according to actual needs. In this embodiment, the feedforward wind speed may be obtained by weighted summing the impeller equivalent wind speeds corresponding to the obtained multiple distance sections and the set weighted factor.

[0068] Specifically, in this embodiment, the effective impeller equivalent wind speeds at different distance sections at a specified time are obtained as V1, V2, V3, V4...Vn, respectively, and the weighting factors are set as p1, p2, p3, p4...pn, respectively. The impeller equivalent wind speeds are weighted and summed to obtain the feedforward wind speed V=V1*p1+V2*p2+V3*p3+...+Vn*pn, where p1+p2+p3+p4+...+pn=1, p1=p2=p3=p4=...=pn=1 / n.

[0069] Through such a setting in the present embodiment, the delay parameters can be estimated in combination with the characteristics of the wind turbine and the lidar, and the axial projection wind speeds of multiple distance sections can be weightedly combined to realize the calculation of the feedforward wind speed, providing effective data support for the precise feedforward control of the wind turbine, and transmitting the real-time calculated feedforward wind speed V to the wind turbine's pitch control system, and realizing the wind turbine's pitch control according to different time filtering parameters, so as to facilitate the wind turbine pitch control based on the feedforward wind speed amplitude wind turbine set, thereby improving the accuracy and effectiveness of the control parameters.

[0070] In this embodiment, optionally, the radar data in the first time period includes wind speed data at hub height, upper plane wind speed data, lower plane wind speed data and radial wind speed data; determining extreme wind conditions based on the radar data in the first time period includes: determining an extreme gust mark based on the wind speed data at hub height; determining an extreme shear mark based on the upper plane wind speed data and the lower plane wind speed data; and determining an extreme turbulence mark based on the radial wind speed data.

[0071] The hub height wind speed data may include the hub height wind speed and the hub height wind speed flag. The hub height wind speed may refer to the wind speed at the hub height of the wind turbine generator set. The hub height wind speed flag may be used to indicate whether the hub height wind speed is valid. Figure 4 As shown, the airport radar can form an upper plane and a lower plane in different directions of four light beams. Specifically, the direction of light beam 1 and the direction of light beam 2 form an upper plane, and the direction of light beam 3 and the direction of light beam 4 form a lower plane. The upper plane wind speed data may include the upper plane wind speed and the upper plane wind speed flag. In this embodiment, due to the position and shape of the cabin, the radar can measure wind speeds at different heights, which may include the wind speed of the upper plane and the wind speed of the lower plane. The upper plane wind speed refers to the wind speed at a certain distance above the wind rotor of the wind turbine generator set. The upper plane wind speed flag can be used to identify whether the upper plane wind speed is valid. The lower plane wind speed data may include the lower plane wind speed and the lower plane wind speed flag. The lower plane wind speed may refer to the wind speed at a certain distance below the wind rotor of the wind turbine generator set. The lower plane wind speed flag may be used to identify whether the lower plane wind speed is valid. The radial wind speed data may include the radial wind speed and the radial wind speed flag. The radial wind speed data may refer to the speed component of the wind along the line between the radar and the measurement point. The radial wind speed flag may be used to identify whether the radial wind speed is valid.

[0072] In this embodiment, the wind speed average of all valid distance sections at the current moment can be determined according to the wind speed at the hub height of each distance section and the wind speed mark at the hub height, the number of distance sections can be determined according to the wind speed average, and whether extreme gust wind conditions occur can be judged according to the number to determine the extreme gust mark. In this embodiment, the wind speed average of the upper plane of each distance section can be determined according to the upper plane wind speed of each distance section and the upper plane wind speed mark, and the wind speed average of the lower plane of each distance section can be determined according to the lower plane wind speed and the lower plane wind speed mark, and whether extreme shear wind conditions occur can be judged according to the upper plane wind speed average and the lower plane wind speed average to determine the extreme shear mark. In this embodiment, the turbulence intensity of a single distance section can be calculated according to the radial wind speed of each distance section and the radial wind speed mark, and the average turbulence intensity of all distance sections can be calculated according to the turbulence intensity of a single distance section, so as to determine whether extreme turbulence wind conditions occur according to the average turbulence intensity of all distance sections and the set threshold, so as to determine the extreme turbulence mark.

[0073] Through such a setting in the present embodiment, the extreme gust mark, extreme shear mark and extreme turbulence mark of extreme wind conditions can be identified and determined according to the wind speeds of multiple distance sections under different states, and the data fields of different distance sections output by the lidar are integrated and different time statistical windows are selected to achieve early identification of extreme wind conditions before they reach the impeller surface, thereby playing a role in protecting the wind turbine.

[0074] In this embodiment, the extreme gust flag, extreme shear flag, and extreme turbulence flag that can be calculated in real time are transmitted to the wind turbine's pitch control system, and the wind turbine's pitch control is implemented according to different time filtering parameters, providing a direct and effective data source for the wind turbine's pitch control system, and creating concrete conditions for the wind turbine to efficiently capture wind energy.

[0075] In this embodiment, optionally, an extreme gust flag is determined based on the wind speed data at the hub height, including: determining a first wind speed mean of multiple distance sections at the current moment and a second wind speed mean of a single distance section within a first time window based on the wind speed data at the hub height; determining a third wind speed mean based on the first wind speed mean and the first time window; determining the number of target distance sections based on the second wind speed mean, the third wind speed mean and a first set threshold; and determining the extreme gust flag based on the number of target distance sections.

[0076] Among them, the first wind speed mean can be the wind speed mean of the wind speed at the hub height of all valid distance sections determined at the current moment. The first time window can be a pre-set statistical time window T1. In this embodiment, the first time window can be set to 6 seconds. The second wind speed mean can be understood as the wind speed mean of the wind speed at the hub height of a single distance section within the first time window. The third wind speed mean can be the mean of the first wind speed means corresponding to all distance sections. The first set threshold can be a pre-set threshold. For example, the first set threshold in this embodiment can be 10m / s, and can also be set according to actual needs. The number of target distance sections can be understood as the number of distance sections determined to meet the specified conditions.

[0077] In this embodiment, the number of target distance sections can be the mean difference obtained by subtracting the second wind speed mean and the third wind speed mean, comparing the mean difference with the first set threshold, and determining the number of all distance examples based on the comparison result. In this embodiment, the number of target distance sections and the total number of distance sections of the laser radar can be compared to determine whether there is an extreme gust wind condition, so as to determine the extreme gust mark.

[0078] Specifically, in this embodiment, the wind speed HWS_hub at the hub height and the wind speed flag HWS_hub_Status at the hub height are obtained from the wind speed data and wind direction data obtained in the second time period corresponding to multiple distance sections. The mean M_HWS_hub of all valid distance sections HWS_hub at the current moment is calculated according to the wind speed HWS_hub at the hub height and the wind speed flag HWS_hub_Status at the hub height, that is, the first wind speed mean is determined as follows:

[0079]

[0080] In this embodiment, the first time window T1 is selected for statistics, and the mean value HWS_hub_T1 of the single distance section HWS_hub in the first time window T1 is calculated according to the wind speed at the hub height HWS_hub and the wind speed flag at the hub height HWS_hub_Status. That is, the second wind speed mean value can be determined in the following manner:

[0081]

[0082] In this embodiment, the mean value M_HWS_hub_T1 of all distance section wind speed mean values ​​M_HWS_hub is determined according to the first wind speed mean value M_HWS_hub and the selected first time window, that is, the third wind speed mean value can be determined in the following manner:

[0083]

[0084] In this embodiment, the specific method of determining the number of target distance sections according to the second wind speed mean, the third wind speed mean and the first set threshold Th1 is to determine the mean difference between the second wind speed mean and the third wind speed mean, compare the mean difference with the first set threshold, and screen out all distance section counts Count_T1 that meet (HWS_hub_T1-M_HWS_hub_T1)>Th1, that is, determine the number of target distance sections. If the target distance section number Count_T1>0.5*n, then output the extreme gust flag Flag1=1, otherwise, the extreme gust flag Flag1=0, where n represents the total number of distance sections of the laser radar. In the example of the present invention, the value of Th1 can be 10m / s, and the statistical time window T1=6 seconds.

[0085] In this embodiment, through such a setting, the extreme gust identifier can be determined and transmitted to the pitch control of the wind turbine, so as to improve the reliability of the pitch control of the wind turbine under extreme wind conditions and ensure data accuracy.

[0086] In this embodiment, optionally, an extreme shear flag is determined based on the upper plane wind speed data and the lower plane wind speed data, including: determining a first upper plane wind speed mean and a first lower plane wind speed mean of multiple distance sections at the current moment respectively based on the upper plane wind speed data and the lower plane wind speed data; determining a second upper plane wind speed mean and a second lower plane wind speed mean of the distance section in a second time window respectively based on the first upper plane wind speed mean and the first lower plane wind speed mean; determining a mean difference based on the second upper plane wind speed mean and the second lower plane wind speed mean; and determining an extreme shear flag based on the mean difference, a second set threshold, and a third set threshold.

[0087] Among them, the first upper plane wind speed mean may refer to the wind speed mean of all distance sections determined according to the upper plane wind speed and the upper plane wind speed flag. The first lower plane wind speed mean may refer to the wind speed mean of all distance sections determined according to the lower plane wind speed and the lower plane wind speed flag. The second time window may be a pre-set statistical time window T2, and the second time window in this embodiment may be set to 15 seconds. The second upper plane wind speed mean may refer to the mean of the first upper plane wind speed mean in the second time window. The second lower plane wind speed mean refers to the mean of the first lower plane wind speed mean in the second time window. The mean difference may be the difference determined by subtracting the second upper plane wind speed mean from the second lower plane wind speed mean. The second set threshold and the third set threshold may be pre-set thresholds. In this embodiment, the value of the second set threshold Th2 may be 5m / s, and the value of the third set threshold Th3 may be 10m / s, and may also be set according to actual needs.

[0088] In this embodiment, the upper plane wind speed HWShigh, the upper plane wind speed flag HWShigh_Status, the lower plane wind speed HWSlow, and the lower plane wind speed flag HWSlow_Status are obtained from the wind speed data and wind direction data obtained in the second time period corresponding to multiple distance sections. In this embodiment, the mean M_HWShigh of all valid distance sections HWShigh at the current moment can be calculated based on the upper plane wind speed data, that is, the determination method of the first upper plane wind speed mean can be:

[0089]

[0090] In this embodiment, the mean value M_HWSlow of all valid distance sections HWSlow at the current moment is determined according to the lower plane wind speed data, that is, the first lower plane wind speed mean value can be determined in the following manner:

[0091]

[0092] In this embodiment, the second upper plane wind speed mean of the distance section in the second time window can be determined according to the first upper plane wind speed mean. Specifically, the second time window T2 is selected for statistics, and the mean M_HWShigh_T2 of all the distance section wind speed mean M_HWShigh in the second time window T2 is calculated, that is, the second upper plane wind speed mean is determined by:

[0093]

[0094] In this embodiment, the second lower plane wind speed mean of the distance section in the second time window can be determined according to the first lower plane wind speed mean. Specifically, the second time window T2 is selected for statistics, and the mean M_HWSlow_T2 of all distance section wind speed mean values ​​M_HWSlow in the second time window T2 is calculated. That is, the second lower plane wind speed mean can be determined by:

[0095]

[0096] In this embodiment, the second upper plane wind speed mean and the second lower plane wind speed mean can be subtracted to obtain the mean difference, and the mean difference can be compared with the second set threshold and the third set threshold respectively to determine the extreme shear mark. Specifically, in this embodiment, the mean difference HWScha=(M_HWShigh_T2-M_HWSlow_T2) is calculated. If (HWScha<0&&HWScha*(-1)>Th2)||(HWScha>0&&HWScha>Th3), the extreme shear mark Flag2=1 is output, otherwise, the extreme shear mark Flag2=0. In this embodiment, the value of the second set threshold Th2 can be set to 5m / s, the value of the third set threshold Th3 can be set to 10m / s, and the second statistical time window T2=15 seconds.

[0097] In this embodiment, by such an arrangement, the extreme shear flag is determined and transmitted to the pitch control of the wind turbine, so as to improve the reliability of the pitch control of the wind turbine under extreme wind conditions.

[0098] In this embodiment, optionally, an extreme turbulence identification is determined based on the radial wind speed data, including: determining a first turbulence intensity in the radial direction of a single distance section within a third time window based on the radial wind speed data; determining a first average turbulence intensity at the upper plane height and a second average turbulence intensity at the lower plane height of the single distance section based on the first turbulence intensity; determining a second turbulence intensity of a single distance section at the height of the wind turbine hub based on the first average turbulence intensity and the second average turbulence intensity; determining a third average turbulence intensity of multiple distance sections based on the second turbulence intensity; and determining an extreme turbulence identification based on the third average turbulence intensity and a fourth set threshold.

[0099] The third time window may be a pre-set statistical time window T3, and in this embodiment, the second time window may be set to 60 seconds. The radial direction is the direction in which the radar beam is located; the first turbulence intensity may refer to the turbulence intensity in the radial direction of a single distance section within the time window T3. In this embodiment, the radar includes four beams, and therefore, the first turbulence intensity may include the turbulence intensities of the four beams in different directions.

[0100] In this embodiment, the radial wind speed RWS and the radial wind speed flag RWS_Status are obtained from the wind speed data and wind direction data obtained in the second period corresponding to multiple distance sections. According to the radial wind speed RWS and the radial wind speed flag RWS_Status, the turbulence intensity TI of the radial direction of a single distance section in the selected statistical third time window T3 is calculated. m , m = 1, 2, 3, 4 represent four beam numbers, that is, the first turbulence intensity can be determined by:

[0101]

[0102] In this embodiment, the first average turbulence intensity at the upper plane height of a single distance section is determined according to the turbulence intensity in the direction of light beam 1 and the turbulence intensity in the direction of light beam 2 in the upper plane of the first turbulence intensity, and the second average turbulence intensity at the lower plane height of a single distance section is determined according to the turbulence intensity in the direction of light beam 3 and the turbulence intensity in the direction of light beam 4 in the lower plane of the first turbulence intensity, so as to obtain the first average turbulence intensity TI at the upper and lower plane heights of the single distance section high and the second average turbulence intensity TI low , as shown below:

[0103]

[0104] In this embodiment, the first average turbulence intensity TI at the upper plane height Height_high and the lower plane height Height_low of a single distance section is used. high and the second average turbulence intensity TI low , calculate the second turbulence intensity TI of a single distance section at the fan hub height Height_hub hub The specific method can be:

[0105]

[0106] In this embodiment, the average value M_TI_hub of the turbulence intensity of all distance sections can be calculated based on the second turbulence intensity of a single distance section, that is, the third average turbulence intensity can be determined in the following manner:

[0107]

[0108] Among them, the fourth set threshold value can be a pre-set threshold value and can be set according to actual needs. In this embodiment, the third average turbulence intensity and the fourth set threshold value can be compared to determine whether there is an extreme turbulent wind condition to determine the extreme turbulence flag. Specifically, if the third average turbulence intensity M_TI_hub> the fourth set threshold value Th4, the extreme turbulence flag Flag3=1 is output, otherwise, the extreme turbulence flag Flag3=0. In this embodiment, the fourth set threshold value Th4=0.25 can be set, and the third time window T3=60 seconds can be counted.

[0109] In this embodiment, by such an arrangement, the extreme turbulence flag is determined and transmitted to the pitch control of the wind turbine, so as to improve the reliability of the pitch control of the wind turbine under extreme wind conditions.

[0110] S330, determining wind turbine yaw data according to the radar data of the second period.

[0111] Among them, the radar data of the second period may include the wind speed at the hub height and the wind direction at the hub height; the wind turbine yaw data may be the yaw angle. In this embodiment, the specific method of determining the wind turbine yaw influence data based on the radar data of the second period may be to filter the radar data of the second period based on the set filtering conditions, determine the wind speed at the hub height and the wind direction at the hub height that meet the filtering conditions; and determine the average yaw angle of different wind speed intervals based on the wind speed at the hub height and the wind direction at the hub height.

[0112] Specifically, in this embodiment, the time T can be used as the statistical period, and the 10-minute statistics of the closest distance section in front of the impeller within the statistical period are selected, that is, the wind speed at the hub height HWS_hub, the wind direction at the hub height Dir_hub, the wind speed efficiency at the hub height HWS_hub_AVI, and the wind direction data efficiency at the hub height Dir_hub_AVI in the radar data of the second period, and the data that meet HWS_hub_AVI≥80&&Dir ... For the data of ir_hub>=-45&&Dir_hub<=45, the average value of Dir_hub in the bin is calculated based on the HWS_hub data field according to the bin statistics method, that is, the average yaw angle in different wind speed ranges can be obtained, wherein the bin range of HWS_hub can be 3m / s~16m / s, the bin interval is 0.5m / s, and the time T can be 24 hours, one week, one month, two months, three months, one year, etc., which can be set according to actual application requirements, and this embodiment does not limit this.

[0113] In this embodiment, starting from the health status of the laser radar and combining it with the data field output by the laser radar in real time, the calculation of the yaw angle is realized, providing a direct and effective data source for the yaw control system of the wind turbine and creating concrete conditions for the wind turbine to efficiently capture wind energy.

[0114] S340, performing pitch control and yaw control on the laser radar wind turbine based on the wind turbine pitch data and the wind turbine yaw data.

[0115] The technical solution of the embodiment of the present invention is to obtain radar data corresponding to multiple distance sections of the laser radar; the radar data includes radar data of the first time period and radar data of the second time period; the wind turbine pitch data includes feedforward wind speed and extreme wind conditions; the feedforward wind speed and extreme wind conditions are determined according to the radar data of the first time period; wherein extreme wind conditions include extreme gusts, extreme shear and extreme turbulence; the wind turbine yaw data is determined according to the radar data of the second time period; and the laser radar wind turbine is pitch controlled and yaw controlled based on the wind turbine pitch data and the wind turbine yaw data. This technical solution improves the efficiency of data by using the radar data field output by the laser radar to determine the wind turbine pitch data and the wind turbine yaw data that affect the wind turbine control, so as to assist in the control of the wind turbine.

[0116] Embodiment 3

[0117] Figure 5 FIG. 1 is a schematic diagram of a fan control device based on a laser radar according to Embodiment 3 of the present invention. Figure 5 As shown, the device comprises:

[0118] A radar data acquisition module 510 is used to acquire radar data corresponding to multiple range sections of the laser radar;

[0119] A wind turbine data determination module 520, configured to determine wind turbine pitch data and wind turbine yaw data according to radar data;

[0120] The wind turbine control module 530 is used to perform pitch control and yaw control on the wind turbine of the laser radar based on the wind turbine pitch data and the wind turbine yaw data.

[0121] Optionally, the radar data includes radar data of a first period and radar data of a second period; the wind turbine pitch data includes feedforward wind speed and extreme wind conditions;

[0122] The wind turbine data determination module 520 includes:

[0123] A wind speed and wind condition determination unit, configured to determine a feedforward wind speed and extreme wind conditions according to the radar data of the first period; wherein the extreme wind conditions include extreme gusts, extreme shear, and extreme turbulence;

[0124] The wind turbine yaw data determination unit is used to determine the wind turbine yaw data according to the radar data in the second period.

[0125] Optionally, the radar data for the first period includes an axial projection wind speed and an axial projection wind speed status flag;

[0126] The wind speed and wind condition determination unit is specifically used to determine the overall delay data based on the axial projection wind speed; when the overall delay data and the axial projection wind speed status mark meet the set conditions, determine the impeller equivalent wind speed of multiple distance sections; determine the feedforward wind speed based on the impeller equivalent wind speed of multiple distance sections and the set weighting factor.

[0127] Optionally, the radar data in the first period includes wind speed data at hub height, upper plane wind speed data, lower plane wind speed data, and radial wind speed data;

[0128] Wind speed and wind condition determination unit, including:

[0129] An extreme gust determination subunit, used to determine an extreme gust flag based on the wind speed data at the hub height;

[0130] An extreme shear determination subunit, used to determine an extreme shear flag based on the upper plane wind speed data and the lower plane wind speed data;

[0131] The extreme turbulence determination subunit is used to determine the extreme turbulence identification according to the radial wind speed data.

[0132] Optionally, an extreme gust determination subunit is specifically used to determine, based on the wind speed data at the hub height, the first wind speed mean of multiple distance sections at the current moment and the second wind speed mean of a single distance section within the first time window; determine the third wind speed mean according to the first wind speed mean and the first time window; determine the number of target distance sections according to the second wind speed mean, the third wind speed mean and the first set threshold; and determine the extreme gust identifier according to the number of target distance sections.

[0133] Optionally, an extreme shear determination subunit is specifically used to determine the first upper plane wind speed average and the first lower plane wind speed average of multiple distance sections at the current moment according to the upper plane wind speed data and the lower plane wind speed data; determine the second upper plane wind speed average and the second lower plane wind speed average of the distance section in the second time window according to the first upper plane wind speed average and the first lower plane wind speed average; determine the mean difference according to the second upper plane wind speed average and the second lower plane wind speed average; determine the extreme shear flag according to the mean difference, the second set threshold and the third set threshold.

[0134] Optionally, an extreme turbulence determination subunit is specifically used to determine a first turbulence intensity in the radial direction of a single distance section within a third time window based on the radial wind speed data; wherein the radial direction is the direction in which the radar beam is located; determine a first average turbulence intensity at the upper plane height and a second average turbulence intensity at the lower plane height of a single distance section based on the first turbulence intensity; determine a second turbulence intensity of a single distance section at the height of the wind turbine hub based on the first average turbulence intensity and the second average turbulence intensity; determine a third average turbulence intensity of multiple distance sections based on the second turbulence intensity; and determine an extreme turbulence flag based on the third average turbulence intensity and a fourth set threshold.

[0135] A laser radar-based fan control device provided in an embodiment of the present invention can execute a laser radar-based fan control method provided in any embodiment of the present invention, and has functional modules and beneficial effects corresponding to the execution method.

[0136] Embodiment 4

[0137] Figure 6 1 is a schematic diagram of the structure of an electronic device provided according to Embodiment 4 of the present invention. The electronic device 10 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0138] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0139] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0140] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a fan control method based on a laser radar.

[0141] In some embodiments, the laser radar-based wind turbine control method can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the laser radar-based wind turbine control method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the laser radar-based wind turbine control method in any other appropriate manner (for example, by means of firmware).

[0142] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0143] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0144] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0145] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0146] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0147] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0148] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0149] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A fan control method based on laser radar, characterized in that: include: Obtain radar data corresponding to multiple range sections of the laser radar; Determine wind turbine pitch data and wind turbine yaw data according to the radar data; The wind turbine of the laser radar is subjected to pitch control and yaw control based on the wind turbine pitch data and the wind turbine yaw data.

2. The method according to claim 1, characterized in that The radar data includes radar data of a first period and radar data of a second period; the wind turbine pitch data includes feedforward wind speed and extreme wind conditions; Determining wind turbine pitch data and wind turbine yaw data according to the radar data includes: Determining the feedforward wind speed and extreme wind conditions according to the radar data of the first period; wherein the extreme wind conditions include extreme gusts, extreme shear, and extreme turbulence; The wind turbine yaw data is determined according to the radar data in the second time period.

3. The method according to claim 2, characterized in that The radar data of the first period includes an axial projection wind speed and an axial projection wind speed status mark; Determining the feedforward wind speed according to the radar data in the first period includes: Determining overall time delay data according to the axial projection wind speed; When the overall time-delay data and the axial projection wind speed state flag meet the set conditions, determining the impeller equivalent wind speeds of multiple distance sections; The feedforward wind speed is determined according to the impeller equivalent wind speeds of the plurality of distance sections and a set weighting factor.

4. The method according to claim 2, characterized in that: The radar data of the first period includes wind speed data at hub height, upper plane wind speed data, lower plane wind speed data and radial wind speed data; Determining the extreme wind conditions according to the radar data in the first period includes: determining an extreme gust flag based on the wind speed data at the hub height; Determining an extreme shear flag based on the upper plane wind speed data and the lower plane wind speed data; An extreme turbulence indicator is determined based on the radial wind speed data.

5. The method according to claim 4, characterized in that Determining an extreme gust flag according to the wind speed data at the hub height includes: Based on the wind speed data at the hub height, respectively determine the first wind speed average of multiple distance sections at the current moment and the second wind speed average of a single distance section in the first time window; Determine a third wind speed average value according to the first wind speed average value and the first time window; Determine the number of target distance sections according to the second wind speed average, the third wind speed average and a first set threshold; The extreme gust identifier is determined according to the number of target distance sections.

6. The method according to claim 4, characterized in that Determining an extreme shear flag according to the upper plane wind speed data and the lower plane wind speed data includes: Determine respectively a first upper plane wind speed average and a first lower plane wind speed average of multiple distance sections at the current moment according to the upper plane wind speed data and the lower plane wind speed data; Determine a second upper plane wind speed average and a second lower plane wind speed average of the distance section in the second time window respectively according to the first upper plane wind speed average and the first lower plane wind speed average; Determine a mean difference according to the second upper plane wind speed mean and the second lower plane wind speed mean; An extreme shearing mark is determined according to the mean difference, the second set threshold and the third set threshold.

7. The method according to claim 4, characterized in that Determining an extreme turbulence indicator based on the radial wind speed data includes: Determine a first turbulence intensity in a radial direction of a single distance section within a third time window according to the radial wind speed data; wherein the radial direction is the direction in which the radar beam is located; Determine a first average turbulence intensity at an upper plane height and a second average turbulence intensity at a lower plane height of a single distance section according to the first turbulence intensity; Determine a second turbulence intensity of a single distance cross section at the height of the wind turbine hub according to the first average turbulence intensity and the second average turbulence intensity; Determining a third average turbulence intensity of a plurality of distance sections according to the second turbulence intensity; An extreme turbulence flag is determined according to the third average turbulence intensity and a fourth set threshold.

8. A fan control device based on laser radar, characterized in that: include: A radar data acquisition module, used to acquire radar data corresponding to multiple range sections of the laser radar; A wind turbine data determination module, used to determine wind turbine pitch data and wind turbine yaw data according to the radar data; A wind turbine control module is used to perform pitch control and yaw control on the wind turbine of the laser radar based on the wind turbine pitch data and the wind turbine yaw data.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the laser radar-based wind turbine control method described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the laser radar-based wind turbine control method described in any one of claims 1 to 7 when executed.

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