CN110658519A - Wind turbine generator blade state monitoring method based on external radiation source radar - Google Patents

Wind turbine generator blade state monitoring method based on external radiation source radar Download PDF

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CN110658519A
CN110658519A CN201910881907.6A CN201910881907A CN110658519A CN 110658519 A CN110658519 A CN 110658519A CN 201910881907 A CN201910881907 A CN 201910881907A CN 110658519 A CN110658519 A CN 110658519A
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wind turbine
blade
distance
turbine generator
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CN110658519B (en
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万显荣
占伟杰
易建新
谢德强
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Wuhan University WHU
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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
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • 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
    • G01S7/02—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00
    • G01S7/41—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S13/00 using analysis of echo signal for target characterisation; Target signature; Target cross-section

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Abstract

The invention discloses a wind turbine blade state monitoring method based on an external radiation source radar. Firstly, preprocessing a direct wave signal and a radar echo signal received by an external radiation source radar to obtain distance spectrum data, and then obtaining the distance spectrum data on a distance unit where a wind turbine generator is located by combining the prior information such as the positions of a transceiver station and the wind turbine generator; sequentially carrying out time domain autocorrelation, FFT (fast Fourier transform) and short-time Fourier transform processing on the data to respectively obtain an autocorrelation spectrum, a Doppler spectrum and a frequency map of the blade echo; the blade rotating speed is estimated according to the spectral peak time interval of the autocorrelation spectrum, the blade surface orientation is estimated by using the Doppler spread frequency of the blade echo in the Doppler spectrum, and the blade breakage condition is judged according to the Doppler spread frequency of each blade in a time-frequency graph. The invention relates to a wide-area non-contact monitoring means which can monitor multiple parameters of multiple groups of wind turbine blades simultaneously.

Description

Wind turbine generator blade state monitoring method based on external radiation source radar
Technical Field
The invention relates to the field of external radiation source radar signal processing and the field of wind power monitoring, in particular to a wind turbine generator blade state monitoring method based on an external radiation source radar.
Background
Wind power generation is a clean and efficient new energy source, and is rapidly developed in recent years. Wind power equipment is often erected in remote areas with severe environments, and operation faults are easy to occur. The blade is one of the most prone to failure parts in the wind power equipment, and the damage repair cost of the blade is high and the time consumption is long. In addition, if damaged blades are not maintained in time, rotation imbalance is caused, serious secondary damage to a fan system can be caused, and even the whole fan collapses.
At present, a sensor is usually installed in a wind turbine generator set to collect relevant information such as blade vibration, sound and temperature, so as to realize state monitoring of a fan blade. These are contact monitoring means, each sensor has a limited monitoring range, and a plurality of sensors may be combined to realize effective monitoring. Moreover, the wind turbine generator is often high, so that the installation and replacement of the sensor are time-consuming and labor-consuming. In comparison, radar monitoring has the characteristics of wide area, non-contact and the like, and has a good application prospect.
The external radiation source radar is a new system radar system for detecting targets by using non-cooperative radiation sources emitted by a third party. The method has the characteristics of environmental protection, spectrum conservation, low cost, easy networking and the like. The inherent system characteristics of the external radiation source radar enable the external radiation source radar to have great potential in the application of monitoring the state of the blades of the wind turbine generator. The radar of the external radiation source can carry out long-time coherent accumulation, obtain the blade echo signals of a plurality of rotation periods, improve the signal-to-noise ratio and the frequency resolution of the echo, and is beneficial to capturing the micro-motion of the blade; (2) the double-base structure with separated receiving and transmitting provides more freedom for acquiring target information, and meanwhile, information complementation is easy to realize through multi-station networking. Therefore, the method has important theoretical significance and practical value for exploring and utilizing the external radiation source radar to monitor the state of the wind turbine blade.
The additional frequency modulation generated by the rotation of the wind turbine blades on the radar echo signal is called micro-Doppler effect. The micro Doppler effect of the wind turbine generator blade is closely related to factors such as structural parameters, operation states and the like of the wind turbine generator blade, such as the number of the blades, the length of the blades, the rotating speed of the blades, the orientation of the blades and the like. The invention fully exploits the application potential of the micro Doppler effect of the wind turbine blade in the wind turbine blade state monitoring, expands the application range of the external radiation source radar and the micro Doppler effect, and provides a good alternative or supplement scheme for the wind turbine monitoring.
Disclosure of Invention
On the basis of analyzing the advantages and the disadvantages of the existing wind turbine blade state monitoring method in detail, the invention aims to provide a wide-area non-contact wind turbine blade state monitoring method and device by utilizing the specific advantages of an external radiation source radar. The method makes full use of the connection between the micro Doppler echo characteristics of the blades of the wind turbine generator set and the structural parameters and the motion state of the blades. The technical scheme of the invention is as follows:
a wind turbine generator blade state monitoring method based on an external radiation source radar comprises the following steps:
step 1: the method comprises the steps that a reference antenna of an external radiation source radar system is pointed to a transmitting station and used for receiving a direct wave signal, a monitoring antenna of the external radiation source radar system is pointed to a wind turbine generator and used for receiving a radar echo signal, preprocessing the direct wave signal and the radar echo signal and obtaining distance spectrum data;
step 2: determining a distance unit of a wind turbine blade echo in a distance spectrum according to the positions of a radar transceiver station and a wind turbine, and acquiring distance spectrum data on the distance unit;
and step 3: performing time domain autocorrelation processing on distance spectrum data on a distance unit where the wind turbine generator is located, and estimating the rotating speed of blades of the wind turbine generator according to the spectral peak time interval of an autocorrelation result;
and 4, step 4: performing FFT (fast Fourier transform) on distance spectrum data on a distance unit where the wind turbine generator is located to obtain a Doppler spectrum of blade echoes, obtaining Doppler expansion frequency of the blade echoes from the spectrum, and estimating the blade surface orientation of the blade;
and 5: short-time Fourier transform is carried out on distance spectrum data on a distance unit where the wind turbine generator is located, a time-frequency graph of blade echoes is obtained, and the breakage condition of the blades is determined according to Doppler expansion frequency of each blade in the time-frequency graph;
preferably, the signal preprocessing described in step 1 specifically includes:
purifying a reference channel direct wave signal, monitoring clutter suppression and matched filtering in a channel radar echo signal;
the direct wave after signal preprocessing is set as ref (t)f,ts) The radar echo is echo (t)f,ts) Wherein t isfFor a fast time, tsFor slow times, the distance spectrum data of the radar echo can be represented as:
Figure BDA0002206122920000021
wherein,
Figure BDA0002206122920000022
representing fast edge time tfThe fast fourier transform is performed and the fast fourier transform,
Figure BDA0002206122920000023
representing fast edge time tfThe complex conjugate transformation is carried out, and the complex conjugate transformation,
Figure BDA0002206122920000024
representing fast edge time tfPerforming inverse fast Fourier transform, wherein R represents a fast time dimension of the distance spectrum, and D represents a slow time dimension of the distance spectrum;
preferably, the step 2 of determining a distance cell of the wind turbine blade echo in the distance spectrum according to the positions of the radar transceiver station and the wind turbine, and acquiring distance spectrum data on the distance cell includes the following steps:
the step 2 of determining the distance unit of the wind turbine blade echo in the distance spectrum specifically comprises the following steps:
step 2.1: by using the distance L between the radar receiving station and the transmitting station (base length) and the distance L from the ith wind turbine generator to the radar receiving stationi,r-wAnd the firstDistance L between i wind turbines and transmitting stationi,t-wAnd obtaining a distance element of the ith wind turbine blade echo in the distance spectrum, wherein the distance element is represented as follows:
Figure BDA0002206122920000031
wherein i is more than or equal to 1 and less than or equal to M, M is the total number of the monitored wind turbine generators, fsRepresents the sampling rate (unit: Hz) of the radar system, c is the speed of light, and round (·) represents the integer value of the expression result closest to the bracket;
the step 2 of obtaining distance spectrum data on the distance cell specifically includes:
step 2.2: distance spectrum data on a distance element where the ith wind turbine generator is located are obtained and expressed as follows:
dati＝r(RngBini,:),1≤i≤M
wherein dat isi A 1 × D dimension vector, D being the slow time dimension of the distance spectrum;
preferably, the performing time-domain autocorrelation on the distance spectrum data on the distance cell where the wind turbine generator is located in step 3 specifically includes:
step 3.1: for distance spectrum data dat on distance unit where ith wind turbine generator is locatediAnd performing autocorrelation to obtain an autocorrelation spectrum of the ith wind turbine blade, wherein the autocorrelation spectrum is represented as follows:
Figure BDA0002206122920000032
Ri(n)＝abs(tmpi(n-D)),n＝1,2,...,2D-1，1≤i≤M
in the step 3, the estimation of the rotating speed of the wind turbine generator blade according to the spectral peak time interval of the autocorrelation result specifically comprises the following steps:
step 3.2: by means of Ri(n) plotting an autocorrelation spectrum from which the time interval T between adjacent correlation peaks is obtainediBy means of TiEstimating the rotation rate of the ith wind turbine blade, specifically expressed as follows:
Figure BDA0002206122920000033
wherein N isiThe number of the blades of the ith wind turbine generator can be obtained by pre-investigation and used as prior information.
Preferably, the performing FFT on the distance spectrum data on the distance cell where the wind turbine generator is located in step 4 specifically includes:
step 4.1: for distance spectrum data dat on distance unit where ith wind turbine generator is locatediPerforming FFT (fast Fourier transform) to obtain a Doppler spectrum of the blade echo of the ith wind turbine generator, and obtaining Doppler expansion frequency f of the blade echo of the ith wind turbine generator from the spectrumi；
In the step 4, the Doppler expansion frequency of the blade echo is obtained from the spectrum, and the estimation of the blade surface orientation of the blade specifically comprises the following steps:
step 4.2: using fiEstimating the blade surface orientation of the ith wind turbine generator blade according to the relation among the blade structure, the radar transceiving station position, the windmill position and other parametersThe specific expression of (a) is as follows:
Figure BDA0002206122920000042
wherein,
Figure BDA0002206122920000043
Figure BDA0002206122920000044
Figure BDA0002206122920000045
Figure BDA0002206122920000046
γi＝arctan(ai,2/ai,1),1≤i≤M
in each of the above formulae, Li,0The original length of the ith wind turbine blade is expressed, and the original length is obtained by looking up the related data of the wind turbine with the corresponding model or field investigation and can be used as prior information;
Figure BDA0002206122920000049
the estimated rotation rate of the ith wind turbine blade in the claim 4; λ represents the wavelength of the illumination source signal used by the radar; alpha is alphaBLRepresents the azimuth of the radar baseline relative to true north; h isiRepresenting the height of the rotation center of the ith wind turbine blade;representing the distance between the radar receiving station and the rotation center of the ith wind turbine generator blade;the distance between the transmitting station and the rotation center of the ith wind turbine blade is shown.
Preferably, the step 5 of performing short-time fourier transform on the distance spectrum data on the distance cell where the wind turbine is located to obtain the time-frequency diagram of the blade echo specifically includes:
step 5.1: for distance spectrum data dat on distance unit where ith wind turbine generator is locatediCarrying out short-time Fourier transform to obtain a time-frequency graph of blade echoes of the ith wind turbine generator:
in step 5, determining the breakage condition of each blade according to the Doppler spread frequency of each blade in the time-frequency diagram is as follows:
step 5.2: obtaining Doppler expansion frequency f corresponding to each blade in the ith wind turbine generator from the ith time-frequency graphi,k,k＝1,2,...,NiI is more than or equal to 1 and less than or equal to M, wherein NiIs the number of blades;
step 5.3: according to fi,k,k＝1,2,...,NiAnd determining the breaking condition of the blade according to the relation between i is more than or equal to 1 and less than or equal to M and the length of each blade, wherein the length of the kth blade in the ith wind turbine generator can be expressed as:
Figure BDA0002206122920000051
if L isi,0-Li,k≤ΔL,k＝1,2,...,NiIf i is more than or equal to 1 and less than or equal to M, the k blade of the ith wind turbine generator set is not broken; if L isi,0-Li,k＞ΔL,k＝1,2,...,NiAnd i is more than or equal to 1 and less than or equal to M, the k blade of the ith wind turbine generator is considered to have a breakage condition, and the distance between the breakage position and the rotation center of the blade is Li,k. Wherein, DeltaLIs a threshold value for judging the blade breakage set in consideration of the resolution of the FFT.
Compared with the prior art, the invention has the following characteristics:
the invention can monitor the states of a plurality of wind turbine blades simultaneously, and is a wide-area non-contact monitoring method;
the invention can simultaneously monitor a plurality of parameters such as the rotating speed of the blades of the wind turbine generator, the orientation of the fan surface, the breaking condition of each blade and the like.
Drawings
FIG. 1: is a specific process of the method provided by the invention.
FIG. 2: the method is a schematic diagram of a wind turbine blade monitoring scene based on an external radiation source radar.
FIG. 3: is the distance spectrum of the radar echo in example 1 of the present invention.
FIG. 4: the autocorrelation spectrum of the echo of the blade of the wind turbine generator set in embodiment 1 of the invention is shown.
FIG. 5: the Doppler spectrum of the echo of the blade of the wind turbine generator set in the embodiment 1 of the invention is shown.
FIG. 6: is a time-frequency diagram of the echo of the blades of the wind turbine generator system in embodiment 1 of the invention.
FIG. 7: the doppler spectrum of the radar echo in example 2 of the present invention is shown.
FIG. 8: is the distance spectrum of the radar echo in example 2 of the present invention.
FIG. 9: the autocorrelation spectrum of the echo of the blade of the wind turbine generator set in embodiment 2 of the invention is shown.
FIG. 10: the Doppler spectrum of the echo of the blade of the wind turbine generator set in embodiment 2 of the invention is shown.
FIG. 11: the time-frequency diagram of the echo of the blade of the wind turbine generator set in embodiment 2 of the invention is shown.
Detailed Description
In order to facilitate the understanding and practice of the present invention for those of ordinary skill in the art, the present invention will be described in further detail with reference to the accompanying drawings and examples, it being understood that the examples described herein are for purposes of illustration and explanation only and are not intended to be limiting.
Example 1
The embodiment is a simulation embodiment, wherein an external radiation source radar system is used for monitoring the states of 2 wind turbine blades, the center frequency of an irradiation source signal used in the simulation process is 722MHz, the coherent accumulation time in the processing process is set to 6s, and other detailed simulation parameters are shown in the following table:
table 1 parameters of the simulation of the embodiment
Figure BDA0002206122920000061
The invention is used for monitoring the state of the blades of the wind turbine generator, the basic signal processing flow is shown in figure 2, and the method specifically comprises the following steps:
step 1: the method comprises the steps that a reference antenna of an external radiation source radar system is pointed to a transmitting station and used for receiving a direct wave signal, a monitoring antenna of the external radiation source radar system is pointed to a wind turbine generator and used for receiving a radar echo signal, preprocessing the direct wave signal and the radar echo signal and obtaining distance spectrum data;
in the embodiment, the direct wave signal is a simulated signal without the echo of the blade of the wind turbine, and is represented as ref (t)f,ts) The monitoring channel signal is the superposition of echo signals of two wind turbine blades and is represented as echo (t)f,ts) Wherein t isfFor a fast time, tsFor slow times, the distance spectrum data can be represented as:
Figure BDA0002206122920000062
wherein,
Figure BDA0002206122920000063
representing fast edge time tfThe fast fourier transform is performed and the fast fourier transform,
Figure BDA0002206122920000064
representing fast edge time tfThe complex conjugate transformation is carried out, and the complex conjugate transformation,
Figure BDA0002206122920000065
representing fast edge time tfAnd performing inverse fast fourier transform, wherein R represents a fast time dimension of the distance spectrum, and D represents a slow time dimension of the distance spectrum, the fast time dimension R is set to 200, and the slow time dimension D is set to 10800 in this embodiment, and fig. 3 shows the distance spectrum of the radar echo simulated in this embodiment.
Step 2: according to the positions of a radar transceiver station and a wind turbine generator, determining a distance cell of a wind turbine generator blade echo in a distance spectrum, and acquiring distance spectrum data on the distance cell, the method specifically comprises the following steps:
step 2.1: by using the distance L between the radar receiving station and the transmitting station (base length) and the distance L from the ith wind turbine generator to the radar receiving stationi,r-wAnd the distance L between the ith wind turbine generator and the transmitting stationi,t-wAnd obtaining a distance element of the ith wind turbine blade echo in the distance spectrum, wherein the distance element is represented as follows:
wherein i is more than or equal to 1 and less than or equal to M, M is the total number of the monitored wind turbine generators, fsRepresents the sampling rate (unit: Hz) of the radar system, c is the speed of light, and round (·) represents the integer value of the expression result closest to the bracket; according to the simulation parameters shown in table 1, the following can be calculated: l ≈ 20.06km, L1,r-w≈1.46km、L1,t-w≈21.47km、L2,r-w≈3.49km、L2,t-wAbout 23.55km with fs＝7.56×106Hz，c＝3×108m/s, so that RngBin1＝72、RngBin2＝176。
Step 2.2: distance spectrum data on a distance element where the ith wind turbine generator windmill is located are obtained and expressed as follows:
dati＝r(RngBini,:),1≤i≤M
wherein dat isiIs a 1 × D dimension vector, D is the slow time dimension of the distance spectrum, dat in this example1＝r(72,:)、dat2R (176, c), the slow time dimension D10800.
And step 3: the method comprises the following steps of performing time domain autocorrelation processing on distance spectrum data on a distance unit where a wind turbine generator is located, and estimating the rotating speed of a blade of the wind turbine generator according to a spectrum peak time interval of an autocorrelation result, wherein the method specifically comprises the following steps:
step 3.1: for distance spectrum data dat on distance unit where ith wind turbine generator is locatediAnd performing autocorrelation to obtain an autocorrelation spectrum of the ith wind turbine blade, wherein the autocorrelation spectrum is represented as follows:
Figure BDA0002206122920000072
Ri(n)＝abs(tmpi(n-D)),n＝1,2,...,2D-1，1≤i≤M
step 3.2: by means of Ri(n) plotting an autocorrelation spectrum from which the time interval T between adjacent correlation peaks is obtainediBy means of TiEstimating the rotation rate of the ith wind turbine blade, specifically expressed as follows:
Figure BDA0002206122920000081
wherein N isiThe number of the blades of the ith wind turbine generator can be obtained by pre-investigation and used as prior information. FIG. 4 shows the autocorrelation result of the echo of the blade of the wind turbine, from which T can be derived1≈1.667s、T2Approximately equals to 2.100s, so that the rotating speeds of the two wind turbine blades can be estimated to be respectively
Figure BDA0002206122920000082
And 4, step 4: the method comprises the following steps of carrying out FFT (fast Fourier transform) on distance spectrum data on a distance unit where a wind turbine generator is located to obtain a Doppler spectrum of blade echoes, obtaining Doppler expansion frequency of the blade echoes from the spectrum, and estimating blade surface orientation of blades, and specifically comprises the following steps:
step 4.1: for distance spectrum data dat on distance unit where ith wind turbine generator is locatediPerforming FFT (fast Fourier transform) to obtain a Doppler spectrum of the blade echo of the ith wind turbine generator, and obtaining Doppler expansion frequency f of the blade echo of the ith wind turbine generator from the spectrumi；
Step 4.2: using fiEstimating the blade surface orientation of the ith wind turbine generator blade according to the relation among the blade structure, the radar transceiving station position, the windmill position and other parameters
Figure BDA0002206122920000083
The specific expression of (a) is as follows:
wherein,
Figure BDA0002206122920000085
Figure BDA0002206122920000086
Figure BDA0002206122920000087
Figure BDA0002206122920000088
Figure BDA0002206122920000089
Figure BDA00022061229200000810
γi＝arctan(ai,2/ai,1),1≤i≤M
in each of the above formulae, Li,0The original length of the ith wind turbine blade is expressed, and the original length is obtained by looking up the related data of the wind turbine with the corresponding model or field investigation and can be used as prior information;the estimated rotation rate of the ith wind turbine blade in the claim 4; λ represents the wavelength of the illumination source signal used by the radar; alpha is alphaBLRepresents the azimuth of the radar baseline relative to true north; h isiRepresenting the height of the rotation center of the ith wind turbine blade;
Figure BDA0002206122920000092
representing the distance between the radar receiving station and the rotation center of the ith wind turbine generator blade;
Figure BDA0002206122920000093
the distance between the transmitting station and the rotation center of the ith wind turbine blade is shown. FIG. 5 shows Doppler spectrums of two wind turbine blade echoes, from which it can be seen that Doppler spread frequencies of the two wind turbine blade echoes are respectively f1≈229.62Hz，f2Approximately equals to 98.30Hz, and the blade rotor is obtained by estimating each simulation parameter and the step 3 shown in the table 1Calculating the result of each expression in the step 4.2, and finally estimating to obtain the orientation of the two wind turbine blades respectively
Figure BDA0002206122920000095
And 5: the method comprises the following steps of carrying out short-time Fourier transform on distance spectrum data on a distance unit where a wind turbine generator is located to obtain a time-frequency graph of blade echoes, and determining the breakage condition of blades according to Doppler expansion frequency of each blade in the time-frequency graph, wherein the method specifically comprises the following steps:
step 5.1: for distance spectrum data dat on distance unit where ith wind turbine generator is locatediCarrying out short-time Fourier transform to obtain a time-frequency graph of blade echoes of the ith wind turbine generator;
step 5.2: obtaining Doppler expansion frequency f corresponding to each blade in the ith wind turbine generator from the ith time-frequency graphi,k,k＝1,2,...,NiI is more than or equal to 1 and less than or equal to M, wherein NiIs the number of blades;
step 5.3: according to fi,k,k＝1,2,...,NiAnd determining the breaking condition of the blade according to the relation between i is more than or equal to 1 and less than or equal to M and the length of each blade, wherein the length of the kth blade in the ith wind turbine generator can be expressed as:
Figure BDA0002206122920000096
if abs (L)i,0-Li,k)≤Δi,L,k＝1,2,...,NiIf i is more than or equal to 1 and less than or equal to M, the k blade of the ith wind turbine generator set is not broken; if abs (L)i,0-Li,k)＞Δi,L,k＝1,2,...,NiAnd i is more than or equal to 1 and less than or equal to M, the k blade of the ith wind turbine generator is considered to have a breakage condition, and the distance between the breakage position and the rotation center of the blade is Li,k. Wherein, Deltai,LIs a threshold value for judging the blade breakage set in consideration of the resolution of the FFT. FIG. 6 showsThe time-frequency graph of the echoes of the two wind turbine blades can obtain Doppler expansion frequencies f corresponding to the blades of the two wind turbine blades respectively1,1≈130.10Hz、f1,2≈105.50Hz、f1,3≈228.50Hz、f2,1≈98.44Hz、f2,2≈28.13Hz、f2,3The length of each blade in the two wind turbines is L through calculation1,1≈30.60m、L1,2≈24.81m、L1,3≈53.74m、L2,1≈54.08m、L2,2≈15.45m、L2,3Approximately equal to 54.08 m. In the embodiment, the frequency resolution of the short-time Fourier transform is set to be 3Hz, and delta is set by combining the maximum Doppler expansion frequencies of the two wind generation sets1,L＝0.71m，Δ2,L1.65 m. Therefore, the condition that the wind turbine generator 1 has two blades with broken loss can be obtained, the distance between the broken loss position and the rotating center of the blades is respectively 30.60m and 24.81m, the distance between the broken loss position and the rotating center of the blades is 15.45m, and the distance between the broken loss position and the rotating center of the blades is one blade with broken loss.
The rotating speed, the blade surface orientation and the breakage condition of each blade of the wind turbine generator set obtained by the method are all consistent with the simulation parameters set in the simulation embodiment, and the method provided by the invention can be used for monitoring the blade states of a plurality of wind turbine generators simultaneously.
Example 2
The present example verifies the effectiveness of the present invention by an external field experiment. The external radiation source radar system used in the embodiment uses a terrestrial digital multimedia broadcasting signal (DTMB) as an opportunity radiation source, the center frequency of the signal is 722MHz, and the bandwidth is 7.56 MHz. The used wind turbine generators are located in the south of Henan Luoyang, 10 wind turbine generators are in the radar detection range, the distance Doppler spectrum of radar echoes is shown in figure 7, and Doppler dimension expansion caused by the micro Doppler effect of the blade echoes of the wind turbine generators can be clearly seen from the figure. The method has great potential in wide-area monitoring of the state of the wind turbine blade by using the external radiation source radar. Not generally, in this embodiment, the specific implementation flow of the present invention is described by taking the monitoring of the blade states of 1 wind turbine generator as an example. The following table presents prior information obtained by field investigation and review of relevant data, which can be used in subsequent computational processes.
TABLE 2 Prior information
Figure BDA0002206122920000101
The radar system is used for receiving direct wave signals by pointing to a transmitting station by a reference antenna, and an antenna array of a monitoring channel points to the area where the wind turbine generator is located and is used for receiving the blade echo of the wind turbine generator. This embodiment and embodiment 1 share the same inventive concept, and for the purpose of detailed description, please refer to embodiment 1. The embodiment specifically comprises the following steps:
step 1: and respectively utilizing a reference channel and a monitoring channel of the external radiation source radar system to receive a direct wave signal of a transmitting station and an echo signal scattered by a blade of the wind turbine generator, preprocessing the signals and obtaining a distance spectrum of radar echo. In this embodiment, the fast time dimension R of the distance spectrum is set to 300, and the slow time dimension D is set to 21600, and fig. 8 shows the distance spectrum of the radar echo of this embodiment, and it can be seen from the spectrum that the modulation band along the slow time dimension corresponds to each wind turbine blade.
Step 2: and determining a distance cell of the wind turbine blade echo in the distance spectrum according to the positions of the radar transceiver station and the wind turbine, and acquiring distance spectrum data on the distance cell. In this embodiment, there are 10 wind turbines, which are respectively located on 3, 31, 72, 89, 102, 132, 149, 177, 258, and 286 distance units of the radar echo distance spectrum, and this embodiment is described by using the wind turbine on the 72 th distance unit for research.
And step 3: time domain autocorrelation processing is carried out on distance spectrum data on a distance unit where a wind turbine generator is located, the rotating speed of a blade of the wind turbine generator is estimated according to the time interval of a spectrum peak of an autocorrelation result, the autocorrelation result of an echo of the blade of a target wind turbine generator is shown in fig. 9, and the time interval between two adjacent correlation spectrum peaks is about 1.607s, so that the blade rotating speed can be estimated to be about 1.303 (rad/s).
And 4, step 4: and performing FFT (fast Fourier transform) on the distance spectrum data on the distance unit where the wind turbine generator is located to obtain the Doppler spectrum of the blade echo, obtaining the Doppler expansion frequency of the blade echo from the spectrum, and estimating the blade surface orientation of the blade. Fig. 10 shows a doppler spectrum of the echo of the blade of the target wind turbine, and it can be seen from the doppler spread frequency of the echo spectrum is about 131Hz, and the blade surface orientation of the wind turbine at this time can be estimated to be about 318 ° by using the blade rotation speed estimated value obtained in step 2 and the priori information in table 2.
And 5: and performing short-time Fourier transform on the distance spectrum data on the distance unit where the wind turbine generator is located to obtain a time-frequency graph of blade echoes, and determining the breakage condition of the blades according to the Doppler expansion frequency of each blade in the time-frequency graph. Fig. 11 shows a time-frequency diagram of the echo of the blade of the target wind turbine, and it can be seen from the diagram that doppler expansions corresponding to 3 blades of the target wind turbine are basically consistent, and it can be considered that the target windmill does not have blade breakage at this time within an error allowable range.
From the above analysis, the parameters of the rotating speed, the blade surface orientation, the blade breakage condition and the like of the blades of the wind turbine generator, which are obtained by using the algorithm provided by the invention, are basically consistent with the actual conditions, and the feasibility of monitoring the blade state of the wind turbine generator by using the algorithm provided by the invention is proved.
It should be understood that parts of the specification not set forth in detail are well within the prior art.
It should be understood that the above-mentioned embodiments are described in some detail, and not intended to limit the scope of the invention, and those skilled in the art will be able to make alterations and modifications without departing from the scope of the invention as defined by the appended claims.

Claims (6)

1. A wind turbine generator blade state monitoring method based on an external radiation source radar is characterized by comprising the following steps:
step 1: the method comprises the steps that a reference antenna of an external radiation source radar system is pointed to a transmitting station and used for receiving a direct wave signal, a monitoring antenna of the external radiation source radar system is pointed to a wind turbine generator and used for receiving a radar echo signal, preprocessing the direct wave signal and the radar echo signal and obtaining distance spectrum data;
step 2: determining a distance unit of a wind turbine blade echo in a distance spectrum according to the positions of a radar transceiver station and a wind turbine, and acquiring distance spectrum data on the distance unit;
and step 3: performing time domain autocorrelation processing on distance spectrum data on a distance unit where the wind turbine generator is located, and estimating the rotating speed of blades of the wind turbine generator according to the spectral peak time interval of an autocorrelation result;
and 4, step 4: performing FFT (fast Fourier transform) on distance spectrum data on a distance unit where the wind turbine generator is located to obtain a Doppler spectrum of blade echoes, obtaining Doppler expansion frequency of the blade echoes from the spectrum, and estimating the blade surface orientation of the blade;
and 5: and performing short-time Fourier transform on the distance spectrum data on the distance unit where the wind turbine generator is located to obtain a time-frequency graph of blade echoes, and determining the breakage condition of the blades according to the Doppler expansion frequency of each blade in the time-frequency graph.
2. The wind turbine blade state monitoring method based on the external radiation source radar as claimed in claim 1, wherein the method comprises the following steps: the signal preprocessing in the step 1 specifically comprises:
purifying a reference channel direct wave signal, monitoring clutter suppression and matched filtering in a channel radar echo signal;
the direct wave after signal preprocessing is set as ref (t)f,ts) The radar echo is echo (t)f,ts) Wherein t isfFor a fast time, tsFor slow times, the distance spectrum data of the radar echo can be represented as:
wherein,
Figure FDA0002206122910000012
representing fast edge time tfThe fast fourier transform is performed and the fast fourier transform,
Figure FDA0002206122910000013
representing fast edge time tfThe complex conjugate transformation is carried out, and the complex conjugate transformation,
Figure FDA0002206122910000014
representing fast edge time tfAn inverse fast fourier transform is performed, R represents the fast time dimension of the distance spectrum, and D represents the slow time dimension of the distance spectrum.
3. The wind turbine blade state monitoring method based on the external radiation source radar as claimed in claim 1, wherein the method comprises the following steps: step 2, determining a distance cell of a wind turbine blade echo in a distance spectrum according to the positions of the radar transceiver station and the wind turbine, and acquiring distance spectrum data on the distance cell, specifically comprising the following steps:
the step 2 of determining the distance unit of the wind turbine blade echo in the distance spectrum specifically comprises the following steps:
step 2.1: by using the distance L between the radar receiving station and the transmitting station (base length) and the distance L from the ith wind turbine generator to the radar receiving stationi,r-wAnd the distance L between the ith wind turbine generator and the transmitting stationi,t-wAnd obtaining a distance element of the ith wind turbine blade echo in the distance spectrum, wherein the distance element is represented as follows:
wherein i is more than or equal to 1 and less than or equal to M, M is the total number of the monitored wind turbine generators, fsRepresents the sampling rate (unit: Hz) of the radar system, c is the speed of light, and round (·) represents the integer value of the expression result closest to the bracket;
the step 2 of obtaining distance spectrum data on the distance cell specifically includes:
step 2.2: distance spectrum data on a distance element where the ith wind turbine generator is located are obtained and expressed as follows:
dati＝r(RngBini,:),1≤i≤M
wherein dat isiA 1 x D-dimensional vector and D is the slow time dimension of the distance spectrum.
4. The wind turbine blade state monitoring method based on the external radiation source radar as claimed in claim 1, wherein the method comprises the following steps: the time domain autocorrelation of the distance spectrum data on the distance cell where the wind turbine generator is located in step 3 specifically comprises:
step 3.1: for distance spectrum data dat on distance unit where ith wind turbine generator is locatediAnd performing autocorrelation to obtain an autocorrelation spectrum of the ith wind turbine blade, wherein the autocorrelation spectrum is represented as follows:
Figure FDA0002206122910000022
Ri(n)＝abs(tmpi(n-D)),n＝1,2,...,2D-1，1≤i≤M
in the step 3, the estimation of the rotating speed of the wind turbine generator blade according to the spectral peak time interval of the autocorrelation result specifically comprises the following steps:
step 3.2: by means of Ri(n) plotting an autocorrelation spectrum from which the time interval T between adjacent correlation peaks is obtainediBy means of TiEstimating the rotation rate of the ith wind turbine blade, specifically expressed as follows:
Figure FDA0002206122910000031
wherein N isiThe number of the blades of the ith wind turbine generator can be obtained by pre-investigation and used as prior information.
5. The wind turbine blade state monitoring method based on the external radiation source radar as claimed in claim 1, wherein the method comprises the following steps: the step 4 of performing FFT conversion on the distance spectrum data on the distance cell where the wind turbine generator is located specifically includes:
step 4.1: for distance spectrum data dat on distance unit where ith wind turbine generator is locatediPerforming FFT (fast Fourier transform) to obtain a Doppler spectrum of the blade echo of the ith wind turbine generator, and obtaining Doppler expansion frequency f of the blade echo of the ith wind turbine generator from the spectrumi；
In the step 4, the Doppler expansion frequency of the blade echo is obtained from the spectrum, and the estimation of the blade surface orientation of the blade specifically comprises the following steps:
step 4.2: using fiEstimating the blade surface orientation of the ith wind turbine generator blade according to the relation among the blade structure, the radar transceiving station position, the windmill position and other parameters
Figure FDA0002206122910000032
Figure FDA0002206122910000033
The specific expression of (a) is as follows:
Figure FDA0002206122910000034
wherein,
Figure FDA0002206122910000036
Figure FDA0002206122910000037
Figure FDA0002206122910000038
Figure FDA0002206122910000041
γi＝arctan(ai,2/ai,1),1≤i≤M
in each of the above formulae, Li,0The original length of the ith wind turbine blade is expressed, and the original length is obtained by looking up the related data of the wind turbine with the corresponding model or field investigation and can be used as prior information;
Figure FDA0002206122910000043
the estimated rotation rate of the ith wind turbine blade in the claim 4; λ represents the wavelength of the illumination source signal used by the radar; alpha is alphaBLRepresents the azimuth of the radar baseline relative to true north; h isiRepresenting the height of the rotation center of the ith wind turbine blade;
Figure FDA0002206122910000044
representing the distance between the radar receiving station and the rotation center of the ith wind turbine generator blade;
Figure FDA0002206122910000045
the distance between the transmitting station and the rotation center of the ith wind turbine blade is shown.
6. The wind turbine blade state monitoring method based on the external radiation source radar as claimed in claim 1, wherein the method comprises the following steps: step 5, performing short-time Fourier transform on the distance spectrum data on the distance unit where the wind turbine generator is located, and acquiring a time-frequency diagram of the blade echo specifically comprises the following steps:
step 5.1: for distance spectrum data dat on distance unit where ith wind turbine generator is locatediCarrying out short-time Fourier transform to obtain a time-frequency graph of blade echoes of the ith wind turbine generator:
in step 5, determining the breakage condition of each blade according to the Doppler spread frequency of each blade in the time-frequency diagram is as follows:
step 5.2: obtaining Doppler expansion frequency f corresponding to each blade in the ith wind turbine generator from the ith time-frequency graphi,k,k＝1,2,...,NiI is more than or equal to 1 and less than or equal to M, wherein NiIs the number of blades;
step 5.3: according to fi,k,k＝1,2,...,NiAnd determining the breaking condition of the blade according to the relation between i is more than or equal to 1 and less than or equal to M and the length of each blade, wherein the length of the kth blade in the ith wind turbine generator can be expressed as:
Figure FDA0002206122910000046
if L isi,0-Li,k≤ΔL,k＝1,2,...,NiIf i is more than or equal to 1 and less than or equal to M, the k blade of the ith wind turbine generator set is not broken; if L isi,0-Li,k＞ΔL,k＝1,2,...,NiAnd i is more than or equal to 1 and less than or equal to M, the k blade of the ith wind turbine generator is considered to have a breakage condition, and the distance between the breakage position and the rotation center of the blade is Li,kWherein, isLIs a threshold value for judging the blade breakage set in consideration of the resolution of the FFT.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112629838A (en) * 2020-11-13 2021-04-09 三峡大学 Wind turbine blade fault monitoring method
KR20220066507A (en) * 2020-11-16 2022-05-24 한양대학교 산학협력단 Device and Method for Measuring RPM Using IR-UWB Radar

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2497003A1 (en) * 1980-12-22 1982-06-25 Sanchez Christian Passive tethered radar reflector - uses inflated aerofoil coated with metallic surface to reflect electromagnetic rays and has blade structure for wind stability
CN104865569A (en) * 2015-05-29 2015-08-26 武汉大学 Aircraft target recognition method based on single frequency network passive radar
US20160178731A1 (en) * 2014-12-23 2016-06-23 Thales Holdings Uk Plc Wind turbine rejection in non-scanning radar
CN108387881A (en) * 2018-02-01 2018-08-10 三峡大学 A kind of accurate simulation algorithm of wind turbine blade echo

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
FR2497003A1 (en) * 1980-12-22 1982-06-25 Sanchez Christian Passive tethered radar reflector - uses inflated aerofoil coated with metallic surface to reflect electromagnetic rays and has blade structure for wind stability
US20160178731A1 (en) * 2014-12-23 2016-06-23 Thales Holdings Uk Plc Wind turbine rejection in non-scanning radar
CN104865569A (en) * 2015-05-29 2015-08-26 武汉大学 Aircraft target recognition method based on single frequency network passive radar
CN108387881A (en) * 2018-02-01 2018-08-10 三峡大学 A kind of accurate simulation algorithm of wind turbine blade echo

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
勾月凯 等: "风力发电机组叶片智能巡检系统研究", 《第五届中国风电后市场专题研讨会论文集》 *
唐波 等: "风电机叶片回波模拟及其多普勒特性分析", 《高电压技术》 *
王文娟 等: "基于光纤光栅传感的风电叶片监测技术浅析", 《风能》 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112629838A (en) * 2020-11-13 2021-04-09 三峡大学 Wind turbine blade fault monitoring method
CN112629838B (en) * 2020-11-13 2022-03-08 三峡大学 Wind turbine blade fault monitoring method
KR20220066507A (en) * 2020-11-16 2022-05-24 한양대학교 산학협력단 Device and Method for Measuring RPM Using IR-UWB Radar
KR102421139B1 (en) * 2020-11-16 2022-07-13 한양대학교 산학협력단 Device and Method for Measuring RPM Using IR-UWB Radar

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