CN116540266A - Method for identifying extreme wind conditions based on coherent laser radar - Google Patents
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
- G01S17/95—Lidar systems specially adapted for specific applications for meteorological use
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
- G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
- G01S7/4802—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section
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Abstract
The invention provides a method for identifying extreme wind conditions based on a coherent laser radar. The invention can identify the extreme wind condition by utilizing the coherent laser radar, and expands the application range of the laser radar. Compared with the traditional fixed-distance door adjusting method, the self-adaptive distance door adjusting method can better reflect the wind speed change condition, and therefore the extreme wind condition can be better represented.
Description
Technical Field
The invention relates to the field of laser radar wind measurement, in particular to a method for identifying extreme wind conditions based on a coherent laser radar.
Background
The specific scheme for measuring the wind profile by using the coherent laser radar is as follows: the laser of the laser wind-finding radar generates signal light which is emitted into the air to be detected through the optical antenna and the scanning mechanism, and the signal light reacts with aerosol particles in the air to generate a back scattering signal containing speed information of the back scattering signal. By Doppler principle, utilizing multiple echo signalsThe puller frequency shift is proportional to the velocity of aerosol particle movement (i.e., wind speed) and is specifically expressed as follows:wherein: v is wind speed, lambda is wavelength, f d Is the Doppler shift. Therefore, the backward scattering signal received by the optical antenna is processed to obtain the radial wind speed through beat frequency and digital demodulation of local oscillation light generated by the optical fiber laser in the system.
When the pulse coherent laser radar is used for measuring wind, the distance is judged according to the return time of aerosol at different distances by taking time as a basis, and different distance doors are formed, so that the laser radar can detect the wind speed and wind direction information at different distances.
However, since the existing range gate is relatively fixed, which means that the existing measurement method assumes that the wind field between two range gates is linearly and uniformly changed, and ignores extreme wind conditions and abrupt wind conditions, wind condition identification based on coherent lidar cannot be performed.
In the prior art, the fan main control system is used for identifying the extreme wind conditions through monitoring the fan state, and when the extreme load is detected, the fan starts the protection of the extreme wind conditions, and compared with a coherent laser radar, the sensing mode of the extreme wind conditions is lagged.
Disclosure of Invention
The invention provides a method for identifying extreme wind conditions based on a coherent laser radar, which can identify the extreme wind conditions by using the coherent laser radar and expands the application range of the laser radar.
The invention provides a method for identifying extreme wind conditions based on a coherent laser radar.
The specific identification process is as follows:
1) Setting the nearest distance door Z 0 And the furthest door Z n Collecting wind speeds v0 and vn at corresponding positions to obtain the distance change rate of the wind speed；
2) At Z 0 And Z is n The (n-1) distance gate is arranged between the two, i (1 is more than or equal to i is less than or equal to n-1) distance gate Z i The method meets the following conditions:；
3) Vi and Z i The actual wind speed Vi of the distance gate takes DeltaT as the time interval in the time T, and calculates the variance；
4) For Z i In the T time of the distance gate, taking DeltaT as a time interval, calculating the wind speed gradient，（），vt j For the wind speed at moment j, vt j-1 For the wind speed at moment j-1 and calculate its variance +.>If (if)Then extreme wind conditions are considered to exist at this point from the door;
5) Will Z i-1 To Z i+1 Further identifying according to step 3) and step 4), if identifiedAnd (5) judging the current wind condition as an extreme wind condition if the wind condition is accumulated for more than two times.
The self-adaptive adjusting process of the range gate in the step 2) is specifically as follows:
2.1 Collecting wind speed data of a position corresponding to the initial distance gate as original data; namely, collecting photoelectric signals at an initial distance gate through a telescope of a laser radar, and performing beat frequency by utilizing the photoelectric signals of return light and light-emitting signals;
2.2 Inverting the original data acquired in the step S01 into wind speed, namely, performing frequency domain transformation on photoelectric signals in the step S01 by an FPGA (programmable logic array) through a Fourier transform algorithm;
2.3 2.2) taking the inverted wind speed in the step 2.2) as an input source of the self-adaptive algorithm, and establishing a relation model of the distance difference and the wind speed similarity through the self-adaptive algorithm;
2.4 Correcting the initial distance gate through the relation model to obtain a new distance gate, namely, replacing the initial distance gate by the new distance gate;
2.5 Repeating steps 2.1) to 2.4).
The frequency domain transformation process of the photoelectric signal in step 2.2) is specifically as follows:
2.21 Fourier transform is performed on the data after beat frequency, namely, the time domain signal is converted into a frequency domain signal, and the calculation method is as follows:，/>is a frequency signal>Is a time domain signal, ω is frequency, t is time, < >>Is a complex base;
2.22 The obtained frequency domain signals are accumulated by using a periodogram method, and the formula is as follows:n is the accumulated times, ">Is a frequency signal>Is the frequency of the frequency domain signal,is the power spectrum, ω is the frequency of the power spectrum; the accumulated frequency domain signals show a parabola with a downward opening, and the frequency value at the peak value is Doppler frequency shift;
2.23 Searching the frequency of the peak point i by using a first-order difference method, wherein the first-order difference formula is as follows:；
2.24 According to the followingWherein the frequency corresponding to the peak point i is Doppler shift f d ；
2.25 Using)Calculating a wind speed v, wherein lambda is a wavelength; thereby converting the frequency signal number into wind speed data of each corresponding range gate.
The model building method in the step 2.3) specifically comprises the following steps:
2.31 Wind speed data within s minutes of each range gate is accumulated, wherein any one range gate is expressed as:wherein the jth wind speed is denoted +.>The wind speed set of n distance gates isWherein the ith distance gate is denoted as x i ；/>A wind speed at a j-th time in the i-th range gate;
2.32 Calculating a wind speed data correlation P between two adjacent range gates i ；Wherein->An average wind speed for the ith range gate;
2.33 Calculating the spacing between adjacent range gates, the range gate set representing:for any distance gate in the set +.>Indicating that the interval difference between adjacent distance gates is +.>；
2.34 Through wind speed data correlation P i And the interval difference between adjacent distance gates is calculated to obtain a correlation distance function r i ，Forming a set of correlation distance functions between adjacent distance gates +.>；
2.35 Calculating the mean of the correlation distance function。
Step 2.4) the initial range gate correction process is specifically as follows:
2.41 Correction of threshold value of range gate，/>；
2.42 Judging the correlation distance function r i Whether or not it is greater than the threshold value of the range gateThe method comprises the steps of carrying out a first treatment on the surface of the If r i ＞/>Updating the distance gate; if r i ≤/>The existing range gate is kept unchanged. The updated distance gate is at r i ＞/>In this case, a distance gate ++1 is added between the i-th and i+1-th distance gates>Increased distance door->Taking the average value of the adjacent distance threshold values,,/>represents the i-th distance gate, +.>Represents the (i+1) th distance gate value, when the distance gate is added, the added distance gate +.>Replace->The distance gate shown forms a new +.>A distance gate. The invention also provides a device for performing a method for identifying extreme wind conditions based on coherent lidar, comprising at least a processor and a memory, the memory stores computer-executable instructions, and the processor executes the computer-executable instructions stored in the memory, including data input, data processing, and data processing,And the data output module is used for executing the method for identifying the extreme wind conditions based on the coherent laser radar.
The invention also provides a computer readable storage medium storing a computer program or instructions which, when executed, implement the method for identifying extreme wind conditions based on coherent lidar. The invention has the beneficial effects that:
1. the method can identify the extreme wind conditions by utilizing the coherent laser radar, and expands the application range of the laser radar.
2. Compared with the traditional fixed-distance door adjusting method, the self-adaptive distance door adjusting method can better reflect the wind speed change condition, and therefore the extreme wind condition can be better represented.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings that are needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a wind speed measured by a fixed distance gate;
FIG. 2 is a first set of wind speeds measured by an adaptive range gate;
FIG. 3 is a second set of wind speeds measured by an adaptive range gate.
Description of the embodiments
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention provides a method for identifying extreme wind conditions based on a coherent laser radar.
The specific identification process is as follows:
1) Setting the nearest distance door Z 0 And the furthest door Z n Collecting wind speeds v0 and vn at corresponding positions to obtain the distance change rate of the wind speed；
2) At Z 0 And Z is n The (n-1) distance gate is arranged between the two, i (1 is more than or equal to i is less than or equal to n-1) distance gate Z i The method meets the following conditions:；
2.1 Collecting wind speed data of a position corresponding to the initial distance gate as original data; namely, collecting photoelectric signals at an initial distance gate through a telescope of a laser radar, and performing beat frequency by utilizing the photoelectric signals of return light and light-emitting signals;
2.2 Inverting the original data acquired in the step S01 into wind speed, namely, performing frequency domain transformation on photoelectric signals in the step S01 by an FPGA (programmable logic array) through a Fourier transform algorithm;
2.3 2.2) taking the inverted wind speed in the step 2.2) as an input source of the self-adaptive algorithm, and establishing a relation model of the distance difference and the wind speed similarity through the self-adaptive algorithm;
2.4 Correcting the initial distance gate through the relation model to obtain a new distance gate, namely, replacing the initial distance gate by the new distance gate;
2.5 Repeating steps 2.1) to 2.4);
3) Vi and Z i The actual wind speed Vi of the distance gate takes DeltaT as the time interval in the time T, and calculates the variance；
4) For Z i Taking out in T time of the range gateDelta T is the time interval, and the wind speed gradient is calculated，（）,vt j For the wind speed at moment j, vt j-1 For the wind speed at moment j-1 and calculate its variance +.>If (if)Then extreme wind conditions are considered to exist at this point from the door;
5) Will Z i-1 To Z i+1 Further identifying according to step 3) and step 4), if identifiedAnd (5) judging the current wind condition as an extreme wind condition if the wind condition is accumulated for more than two times.
The frequency domain transformation process of the photoelectric signal in step 2.2) is specifically as follows:
2.21 Fourier transform is performed on the data after beat frequency, namely, the time domain signal is converted into a frequency domain signal, and the calculation method is as follows:，/>is a frequency signal>Is a time domain signal, ω is frequency, t is time, < >>Is a complex base;
2.22 The obtained frequency domain signals are accumulated by using a periodogram method, and the formula is as follows:n is the accumulated times, and the number of times is counted,/>is a frequency signal>Is the frequency of the frequency domain signal,is the power spectrum, ω is the frequency of the power spectrum; the accumulated frequency domain signals show a parabola with a downward opening, and the frequency value at the peak value is Doppler frequency shift;
2.23 Searching the frequency of the peak point i by using a first-order difference method, wherein the first-order difference formula is as follows:；
2.24 According to the followingWherein the frequency corresponding to the peak point i is Doppler shift f d ；
2.25 Using)Calculating a wind speed v, wherein lambda is a wavelength; thereby converting the frequency signal number into wind speed data of each corresponding range gate.
The model building method in the step 2.3) specifically comprises the following steps:
2.31 Wind speed data within s minutes of each range gate is accumulated, wherein any one range gate is expressed as:wherein the jth wind speed is denoted +.>The wind speed set of n distance gates isWherein the ith distance gate is denoted as x i ；/>A wind speed at a j-th time in the i-th range gate;
2.32 Calculating a wind speed data correlation P between two adjacent range gates i ；Wherein->An average wind speed for the ith range gate;
2.33 Calculating the spacing between adjacent range gates, the range gate set representing:for any distance gate in the set +.>Indicating that the interval difference between adjacent distance gates is +.>；
2.34 Through wind speed data correlation P i And the interval difference between adjacent distance gates is calculated to obtain a correlation distance function r i ，Forming a set of correlation distance functions between adjacent distance gates +.>；
2.35 Calculating the mean of the correlation distance function。
Step 2.4) the initial range gate correction process is specifically as follows:
2.41 Correction of threshold value of range gate，/>；
2.42 Judging the correlation distance function r i Whether or not it is greater than the threshold value of the range gateThe method comprises the steps of carrying out a first treatment on the surface of the If r i ＞/>Updating the distance gate; if r i ≤/>The existing range gate is kept unchanged. The updated distance gate is at r i ＞/>In this case, a distance gate ++1 is added between the i-th and i+1-th distance gates>Increased distance door->Taking the average value of the adjacent distance threshold values,,/>represents the i-th distance gate, +.>Represents the (i+1) th distance gate value, when the distance gate is added, the added distance gate +.>Replace->The distance gate shown forms a new +.>A distance gate.
The invention also provides equipment for executing the method for identifying the extreme wind condition based on the coherent laser radar, which at least comprises a processor and a memory, wherein the memory stores computer-executed instructions, and the processor executes the computer-executed instructions stored in the memory and comprises a data input module, a data processing module and a data output module, so that the method for identifying the extreme wind condition based on the coherent laser radar is executed.
The invention also provides a computer readable storage medium storing a computer program or instructions which, when executed, implement the method for identifying extreme wind conditions based on coherent lidar.
The effect of the wind condition recognition method provided in the present application was tested by comparative examples as follows:
fig. 1 is a wind speed measured by a fixed distance gate in 3 months of 2023, fig. 2 and 3 are wind speeds measured by an adaptive distance gate from the end of 2 months to 3 months of 2023, and as compared with fig. 2 and 3, the wind speed curve measured by the adaptive distance gate shows that the wave crest and the wave trough are more obvious than the measured wind speed of the fixed distance gate, so that the wind speed change condition can be better reflected by a method of adjusting the adaptive distance gate, and the extreme wind condition can be better represented.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for the equipment examples, what has been described above is merely a preferred embodiment of the invention, which, since it is substantially similar to the method examples, is described relatively simply, as relevant to the description of the method examples. The foregoing is merely illustrative of specific embodiments of the present invention, and the scope of the invention is not limited thereto, since modifications and substitutions will be readily made by those skilled in the art without departing from the spirit of the invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (9)
1. A method for identifying extreme wind conditions based on coherent lidar is characterized by comprising the following steps: setting a wind field detection range, calculating the uniform change rate of the wind speed in space according to the change quantity of the wind speed along with the distance in the wind field detection range, setting a distance gate, determining the time change rate of the wind speed on the distance gate, comparing the fluctuation amplitude, reducing the wind field range in which the extreme wind condition possibly appears, and finally carrying out cyclic calculation to identify the extreme wind condition.
2. The method for identifying extreme wind conditions based on coherent lidar according to claim 1, wherein the specific identification process is as follows:
1) Setting the nearest distance door Z 0 And the furthest door Z n Collecting wind speeds v0 and vn at corresponding positions to obtain the distance change rate of the wind speed；
2) At Z 0 And Z is n The (n-1) distance gates are arranged between the two, and the self-adaptive adjustment is carried out, so that the i (i is more than or equal to 1) distance gate Z is more than or equal to n-1 i The method meets the following conditions:；
3) Vi and Z i The actual wind speed Vi of the distance gate takes DeltaT as the time interval in the time T, and calculates the variance；
4) For Z i In the T time of the distance gate, taking DeltaT as a time interval, calculating the wind speed gradient，（）,vt j For the wind speed at moment j, vt j-1 Wind speed at j-1 time and meterCalculate its variance->If (if)Then extreme wind conditions are considered to exist at this point from the door;
5) Will Z i-1 To Z i+1 Further identifying according to step 3) and step 4), if identifiedAnd (5) judging the current wind condition as an extreme wind condition if the wind condition is accumulated for more than two times.
3. The method for identifying extreme wind conditions based on coherent lidar according to claim 2, wherein the range gate adaptive adjustment process of step 2) is specifically as follows:
2.1 Collecting wind speed data of a position corresponding to the initial distance gate as original data; namely, collecting photoelectric signals at an initial distance gate through a telescope of a laser radar, and performing beat frequency by utilizing the photoelectric signals of return light and light-emitting signals;
2.2 Inverting the original data acquired in the step S01 into wind speed, namely, performing frequency domain transformation on photoelectric signals in the step S01 by an FPGA (programmable logic array) through a Fourier transform algorithm;
2.3 2.2) taking the inverted wind speed in the step 2.2) as an input source of the self-adaptive algorithm, and establishing a relation model of the distance difference and the wind speed similarity through the self-adaptive algorithm;
2.4 Correcting the initial distance gate through the relation model to obtain a new distance gate, namely, replacing the initial distance gate by the new distance gate;
2.5 Repeating steps 2.1) to 2.4).
4. A method for identifying extreme wind conditions based on coherent lidar according to claim 3, wherein the frequency domain transformation of the optical-electrical signal in step 2.2) is specifically as follows:
2.21 Pair of (a) to (b)The data after beat frequency is subjected to Fourier transformation, namely, a time domain signal is converted into a frequency domain signal, and the calculation method is as follows:，/>is a frequency signal>Is a time domain signal, ω is frequency, t is time, < >>Is a complex base;
2.22 The obtained frequency domain signals are accumulated by using a periodogram method, and the formula is as follows:n is the accumulated times, ">Is a frequency signal>Is the frequency of the frequency domain signal,is the power spectrum, ω is the frequency of the power spectrum; the accumulated frequency domain signals show a parabola with a downward opening, and the frequency value at the peak value is Doppler frequency shift;
2.23 Searching the frequency of the peak point i by using a first-order difference method, wherein the first-order difference formula is as follows:;
2.24 According to the followingWherein the peak point i corresponds to a frequency ofDoppler shift f d ；
2.25 Using)Calculating a wind speed v, wherein lambda is a wavelength; thereby converting the frequency signal number into wind speed data of each corresponding range gate.
5. The method for identifying extreme wind conditions based on coherent lidar according to claim 3 or 4, wherein the model building method of step 2.3) is specifically as follows:
2.31 Wind speed data within s minutes of each range gate is accumulated, wherein any one range gate is expressed as:wherein the jth wind speed is denoted +.>The wind speed set of n distance gates isWherein the ith distance gate is denoted as x i ；/>A wind speed at a j-th time in the i-th range gate;
2.32 Calculating a wind speed data correlation P between two adjacent range gates i ；Wherein->An average wind speed for the ith range gate;
2.33 Calculating the spacing between adjacent range gates, the range gate set representing:in a collection ofFor any distance door>Indicating that the interval difference between adjacent distance gates is +.>；
2.34 Through wind speed data correlation P i And the interval difference between adjacent distance gates is calculated to obtain a correlation distance function r i ，Forming a set of correlation distance functions between adjacent distance gates +.>；
2.35 Calculating the mean of the correlation distance function。
6. The method for identifying extreme wind conditions based on coherent lidar of claim 5, wherein the initial range-gate correction procedure of step 2.4) is specifically as follows:
2.41 Correction of threshold value of range gate，/>；
2.42 Judging the correlation distance function r i Whether or not it is greater than the threshold value of the range gateThe method comprises the steps of carrying out a first treatment on the surface of the If r i ＞/>Then update the distanceA door; if r i ≤/>The existing range gate is kept unchanged.
7. The method for identifying extreme wind conditions based on coherent lidar of claim 6, wherein: the updated distance gate is at r i ＞In this case, a distance gate ++1 is added between the i-th and i+1-th distance gates>Increased range gateTaking the average value of adjacent distance gate values, +.>,/>Represents the i-th distance gate, +.>Represents the (i+1) th distance gate value, when the distance gate is added, the added distance gate +.>Replace->Represented range gate, forming newA distance gate.
8. An apparatus for performing a method of identifying extreme wind conditions based on a coherent lidar, characterized by: at least comprising a processor and a memory, said memory storing computer-executable instructions, said processor executing said computer-executable instructions stored in said memory, including data input, data processing, data output modules, performing the method of identifying extreme wind conditions based on coherent lidar of claim 1.
9. A computer-readable storage medium, characterized by: a computer program or instructions stored which, when executed, implements the method of claim 1 for identifying extreme wind conditions based on coherent lidar.
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Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7557735B1 (en) * | 2006-09-05 | 2009-07-07 | Rockwell Collins, Inc. | Predictive and reactive windshear detection system and method |
| GB0922592D0 (en) * | 2009-12-23 | 2010-02-10 | Vestas Wind Sys As | Method and apparatus for protecting wind turbines from fatigue damage |
| CN103597206A (en) * | 2011-05-06 | 2014-02-19 | 维斯塔斯风力系统集团公司 | Method and apparatus for protecting wind turbines from extreme events |
| US20170010358A1 (en) * | 2015-07-07 | 2017-01-12 | U.S.A. As Represented By The Administrator Of The National Aeronautics And Space Administration | Wind event warning system |
| WO2021079513A1 (en) * | 2019-10-25 | 2021-04-29 | 三菱電機株式会社 | Signal processor, laser radar, and wind turbine |
| CN115657080A (en) * | 2022-12-29 | 2023-01-31 | 南京牧镭激光科技股份有限公司 | Self-adaptive adjustment method for wind measuring distance gate of laser radar |
| CN115951328A (en) * | 2023-03-10 | 2023-04-11 | 中国人民解放军国防科技大学 | Wind speed estimation method and device of wind lidar based on probability density constraint |
| CN116221014A (en) * | 2023-04-28 | 2023-06-06 | 凯宸能源科技(天津)有限公司 | Method, device, system and medium for controlling clearance of wind turbine generator based on laser radar |
-
2023
- 2023-07-06 CN CN202310822785.XA patent/CN116540266B/en active Active
Patent Citations (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US7557735B1 (en) * | 2006-09-05 | 2009-07-07 | Rockwell Collins, Inc. | Predictive and reactive windshear detection system and method |
| GB0922592D0 (en) * | 2009-12-23 | 2010-02-10 | Vestas Wind Sys As | Method and apparatus for protecting wind turbines from fatigue damage |
| WO2011076875A2 (en) * | 2009-12-23 | 2011-06-30 | Vestas Wind Systems A/S | Method and apparatus for protecting wind turbines from fatigue damage |
| CN103597206A (en) * | 2011-05-06 | 2014-02-19 | 维斯塔斯风力系统集团公司 | Method and apparatus for protecting wind turbines from extreme events |
| US20140140843A1 (en) * | 2011-05-06 | 2014-05-22 | Vestas Wind Systems A/S | Method and apparatus for protecting wind turbines from extreme events |
| US20170010358A1 (en) * | 2015-07-07 | 2017-01-12 | U.S.A. As Represented By The Administrator Of The National Aeronautics And Space Administration | Wind event warning system |
| WO2021079513A1 (en) * | 2019-10-25 | 2021-04-29 | 三菱電機株式会社 | Signal processor, laser radar, and wind turbine |
| CN115657080A (en) * | 2022-12-29 | 2023-01-31 | 南京牧镭激光科技股份有限公司 | Self-adaptive adjustment method for wind measuring distance gate of laser radar |
| CN115951328A (en) * | 2023-03-10 | 2023-04-11 | 中国人民解放军国防科技大学 | Wind speed estimation method and device of wind lidar based on probability density constraint |
| CN116221014A (en) * | 2023-04-28 | 2023-06-06 | 凯宸能源科技(天津)有限公司 | Method, device, system and medium for controlling clearance of wind turbine generator based on laser radar |
Non-Patent Citations (2)
| Title |
|---|
| KAI ZHOU, ET AL: "Wind Gust Detection and Impact Prediction for Wind Turbines", REMOTE SENSING, pages 1 - 17 * |
| 李洋: "考虑极端风况预测的风电机组塔架载荷控制策略研究", 中国优秀硕士学位论文全文数据库 工程科技II辑 * |
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