CN111123231A - Wind turbine RCS data compression reconstruction method - Google Patents

Wind turbine RCS data compression reconstruction method Download PDF

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CN111123231A
CN111123231A CN201811296068.3A CN201811296068A CN111123231A CN 111123231 A CN111123231 A CN 111123231A CN 201811296068 A CN201811296068 A CN 201811296068A CN 111123231 A CN111123231 A CN 111123231A
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wind turbine
rcs data
data
rcs
wind
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CN111123231B (en
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唐波
陈昊
刘映彤
李耀伟
奉彭
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China Three Gorges University CTGU
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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
    • G01S7/02—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
    • 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
    • G01S7/40—Means for monitoring or calibrating
    • G01S7/4052—Means for monitoring or calibrating by simulation of echoes
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
    • Y02E10/00—Energy generation through renewable energy sources
    • Y02E10/70—Wind energy
    • Y02E10/72—Wind turbines with rotation axis in wind direction

Abstract

According to a high-frequency electromagnetic scattering theory, the total electromagnetic scattering of the wind turbine can be equivalent to the vector sum of contribution of the electromagnetic scattering at a plurality of local positions, the electromagnetic scattering at the local positions is represented by three parameters including an electric field amplitude value, a scattering coefficient and a coordinate parameter, and the idea that a parameter set formed by combining the strength parameter, the structure type and the position parameter of the electromagnetic scattering of the wind turbine represents a large amount of RCS data of the wind turbine is provided in a targeted manner, so that the effective compression and reconstruction of the RCS data of the wind turbine are realized. In the practical engineering, conditions are created for reducing RCS data storage capacity of the wind turbine, realizing real-time analysis of electromagnetic interference characteristics of the wind turbine to a radar station and carrying out real-time simulation of radar echoes of the wind turbine.

Description

Wind turbine RCS data compression reconstruction method
Technical Field
The invention discloses a method for compressing and reconstructing RCS data of a wind turbine, and belongs to the field of wide-area electromagnetic compatibility of a wind power plant and a radar station system.
Background
The wind power field built in large quantity will inevitably generate serious electromagnetic interference problems to the adjacent radar stations. From the existing research, the RCS data of the wind turbine is an important parameter for analyzing the electromagnetic interference characteristics of radar signals by the wind turbine and simulating the echo of the wind turbine. However, due to the fact that the radar sight line pitch angle, the wind turbine yaw angle and the wind turbine blade rotation angle are changed in real time, and the radar working frequency is usually in a GHz frequency band, the wind turbine RCS data stored in a radar system is huge, generally, the magnitude of the data can reach 1010 orders of magnitude or more, and therefore the wind turbine RCS data storage and real-time rapid analysis effects are directly influenced. Therefore, the method for effectively compressing and quickly reconstructing the RCS data of the wind turbine generator is sought, and the method has important practical significance for reducing data storage capacity, realizing real-time analysis of the electromagnetic scattering characteristics of the wind turbine generator and carrying out real-time simulation research on the radar echo of the wind turbine generator.
In the field of radar target RCS data compression and reconstruction, a currently adopted method is a Threshold Discrete Fourier Transform (TDFT). Although the method has high calculation speed, the effect of compression and reconstruction is directly influenced by the size of the threshold, and the determination of the threshold still has no accurate judgment basis from the current research. Therefore, the application of the conventional TDFT method is greatly limited.
Disclosure of Invention
The invention provides a compression and reconstruction method for RCS data of a wind turbine, which is based on a high-frequency electromagnetic scattering theory and solves the electric field amplitude, scattering coefficient and coordinate parameter of electromagnetic scattering of an air outlet motor from the acquired RCS data of the wind turbine, thereby realizing effective compression and reconstruction of the RCS data of the wind turbine. The method can reduce the storage capacity of RCS data, and has important significance in improving the analysis of the electromagnetic scattering characteristics of the wind turbine and the real-time performance of radar echo simulation of the wind turbine.
The technical scheme adopted by the invention is as follows:
a parameter set formed by combining electric field amplitude, scattering coefficient and coordinate parameter of wind turbine electromagnetic scattering is used for representing a large amount of wind turbine RCS data, and effective compression and reconstruction of the wind turbine RCS data are achieved.
A wind turbine RCS data compression and reconstruction method comprises the following steps:
the method comprises the following steps: and solving RCS data of the wind turbine under different frequency bands and attitude angle conditions, wherein the RCS data comprises amplitude and phase data of the RCS of the wind turbine. In the first step, the operation attitude angle of the wind turbine comprises a radar view line pitch angle, a wind turbine yaw angle and a wind turbine blade rotation angle.
In actual operation of the wind turbine, the operation attitude of the wind turbine changes according to the change of the wind direction, and the bandwidth of the working frequency of the existing radar is large, so that the RCS data of the wind turbine under the conditions of different frequency bands and attitude angles need to be solved. According to the method, the relative position between the wind motor and the radar station is described by three angle combinations of a radar view pitch angle, a wind motor yaw angle and a wind motor blade rotation angle, and RCS data of the wind motor under each angle combination condition are solved. In order to ensure the effect of compression and reconstruction of the RCS data of the wind turbine generator, the phase information of the RCS data of the wind turbine generator is considered.
Step two: and calculating an autocorrelation matrix and a cross-correlation matrix of the RCS data of the wind turbine according to the RCS data of the wind turbine obtained in the first step, solving a conversion matrix between the two matrixes, and further solving a parameter set formed by combining the electromagnetic scattering electric field amplitude, the scattering coefficient and the coordinate parameter of the wind turbine. In the second step, the calculated autocorrelation matrix and cross-correlation matrix are solved by constructing two column vectors according to the RCS data of the wind turbine generator, and a phase difference exists between the two column vectors.
And constructing two column vectors according to the RCS data of the wind turbine generator obtained in the first step, calculating an autocorrelation matrix and a cross-correlation matrix of the RCS data of the wind turbine generator according to the two constructed column vectors, and performing primary eigenvalue decomposition and primary singular value decomposition on the autocorrelation matrix in consideration of possible noise errors in the RCS data of the wind turbine generator obtained in the first step so as to reduce the influence of the noise errors on the solving result of the electromagnetic scattering parameter set of the wind turbine generator. And calculating a conversion matrix between the autocorrelation matrix and the cross-correlation matrix after the noise is removed, and further realizing the solution of the electric field amplitude, the scattering coefficient and the coordinate parameter of the electromagnetic scattering of the wind turbine.
Step three: the wind turbine electromagnetic scattering parameter set solved in the second step is used as compressed data, namely the compression of the wind turbine RCS data is realized; vector superposition is carried out on the wind turbine electromagnetic scattering parameter set, and reconstruction of wind turbine RCS data is achieved. And in the third step, in order to explain the data compression and reconstruction effect of the wind turbine RCS, selecting a data compression ratio and a data reconstruction error as evaluation parameters.
And storing the electric field amplitude, the scattering coefficient and the coordinate parameter which are obtained in the step three as compressed data, so that the RCS data compression of the wind turbine is realized. Vector superposition is carried out on the electric field amplitude, the scattering coefficient and the coordinate parameter of the wind turbine electromagnetic scattering, so that the reconstruction of the RCS data of the wind turbine is realized.
The theoretical basis for realizing the compression and reconstruction of the RCS data of the wind turbine generator through the steps is a high-frequency electromagnetic scattering theory and a vector superposition theory. A parameter set consisting of the electric field amplitude value, the scattering coefficient and the coordinate parameter of the wind turbine electromagnetic scattering is adopted to represent a large amount of wind turbine RCS data, and the problem that the traditional TDFT method is poor in compression reconstruction effect due to the fact that the threshold value is difficult to select is avoided. The wind motor RCS data compression and reconstruction realized by the method saves resources consumed when the wind motor RCS data are stored, and improves the real-time performance of wind motor electromagnetic scattering characteristic analysis and wind motor radar echo simulation on the radar side, which is a key technology and premise for solving the problem of electromagnetic interference of a wind power plant on a radar station.
The invention relates to a wind turbine RCS data compression and reconstruction method, which has the following beneficial effects:
1): the method can solve the problem of poor compression and reconstruction effects caused by difficulty in threshold selection in the traditional method. The method provided by the invention does not need to select the threshold value, and can realize high data compression ratio and low reconstruction error.
2): the problem of large storage resource occupation caused by storage of a large amount of RCS data of the wind turbine under different frequency bands and attitude angles can be solved.
3): the method for compressing and reconstructing the RCS data of the wind turbine can be applied to real-time analysis of electromagnetic scattering characteristics of the wind turbine on the radar side and real-time simulation research of radar echoes of the wind turbine, and early preparation work is performed for solving the problem that the wind turbine generates electromagnetic interference on a radar station.
4): at present, no record is available about the compression and reconstruction of the RCS data of the wind turbine generator, and the traditional TDFT method has poor compression and reconstruction effects due to the difficulty in selecting the threshold value. Therefore, according to the high-frequency electromagnetic scattering theory, the total electromagnetic scattering of the wind turbine can be equivalent to the vector sum of the contribution of the electromagnetic scattering at a plurality of local positions, the electromagnetic scattering at the local positions is represented by three parameters, namely an electric field amplitude value, a scattering coefficient and a coordinate parameter, the invention purposefully provides the idea that a parameter set formed by combining the strength parameter, the structure type and the position parameter of the electromagnetic scattering of the wind turbine represents a large amount of RCS data of the wind turbine, and therefore, the effective compression and reconstruction of the RCS data of the wind turbine are realized. In the practical engineering, conditions are created for reducing RCS data storage capacity of the wind turbine, realizing real-time analysis of electromagnetic interference characteristics of the wind turbine to a radar station and carrying out real-time simulation of radar echoes of the wind turbine.
Drawings
Fig. 1 is an angle schematic diagram of a wind turbine and a radar sight line.
FIG. 2 is a geometric model diagram of a wind turbine.
FIG. 3(a) shows a yaw angle
Figure BDA0001851204210000031
The present invention compresses the reconstruction result graph.
FIG. 3(b) shows a yaw angle
Figure BDA0001851204210000032
The invention reconstructs an error result map.
FIG. 4(a) shows a yaw angle
Figure BDA0001851204210000033
The compressed reconstruction result map of the conventional TDFT method.
FIG. 4(b) shows a yaw angle
Figure BDA0001851204210000034
The reconstructed error result map of the conventional TDFT method.
FIG. 5(a) shows a yaw angle
Figure BDA0001851204210000035
The present invention compresses the reconstruction result graph.
FIG. 5(b) shows a yaw angle
Figure BDA0001851204210000036
The invention reconstructs an error result map.
FIG. 6(a) shows a yaw angle
Figure BDA0001851204210000037
The compressed reconstruction result map of the conventional TDFT method.
FIG. 6(b) shows a yaw angle
Figure BDA0001851204210000038
The reconstructed error result map of the conventional TDFT method.
FIG. 7(a) shows a yaw angle
Figure BDA0001851204210000039
The present invention compresses the reconstruction result graph.
FIG. 7(b) shows a yaw angle
Figure BDA00018512042100000310
The invention reconstructs an error result map.
FIG. 8(a) shows a yaw angle
Figure BDA00018512042100000311
The compressed reconstruction result map of the conventional TDFT method.
FIG. 8(b) shows a yaw angle
Figure BDA00018512042100000312
The reconstructed error result map of the conventional TDFT method.
Detailed Description
A method for compressing and reconstructing RCS (Radar, Cross Section, RCS) data of a wind turbine is characterized in that RCS data of the wind turbine under different frequency bands and attitude angle conditions are solved, the amplitude and phase information of the RCS data of the wind turbine are included, an autocorrelation matrix and a Cross correlation matrix of the RCS data of the wind turbine are calculated, a conversion matrix between the two matrixes is solved, the amplitude, scattering coefficient and coordinate parameter of an electric field of electromagnetic scattering of an air-out motor are solved, compression of the RCS data of the wind turbine is achieved, the parameter set of the electromagnetic scattering of the wind turbine is subjected to vector superposition, and reconstruction of the RCS data of the wind turbine is achieved. Comprises the following steps:
the method comprises the following steps: and solving RCS data of the wind turbine under different frequency bands and attitude angle conditions, wherein the RCS data comprises amplitude and phase data of the RCS of the wind turbine. The wind motor running posture is fully considered to be changed according to the change of the wind direction, so that the relative position relation between the wind motor and the radar sight line is determined by selecting the radar sight line pitch angle, the wind motor yaw angle and the wind motor blade rotation angle, the obtained wind motor RCS data contains all running postures of the wind motor, and the wind motor RCS data not only comprises amplitude information of the wind motor RCS data, but also comprises phase information of the wind motor RCS data. The existing wind turbine RCS data solving algorithm only considers RCS data amplitude information and does not consider RCS data phase information, and therefore a large calculation error exists when a wind turbine electromagnetic scattering parameter set is solved from wind turbine RCS data. The method fully considers the possible operation attitude of the wind turbine, solves the amplitude information and the phase information of the RCS data of the wind turbine, and obviously improves the compression and reconstruction performance of the RCS data of the wind turbine.
Step two: and calculating an autocorrelation matrix and a cross-correlation matrix of the RCS data of the wind turbine according to the RCS data of the wind turbine obtained in the first step, solving a conversion matrix between the two matrixes, and further solving a parameter set formed by combining the electromagnetic scattering electric field amplitude, the scattering coefficient and the coordinate parameter of the wind turbine. The existing parameter set solving method only removes additive complex white gaussian noise through one-time eigenvalue decomposition, and the noise removing method causes large error of the result under the condition of low signal-to-noise ratio. According to the method, singular value decomposition is carried out again on the basis of eigenvalue decomposition, a high-dimensional matrix with pathological eigenvalues is reduced into a low-dimensional matrix without pathological eigenvalues, and the influence of noise on the solving result of the electromagnetic scattering parameter set of the wind turbine is further weakened.
Step three: the solved wind turbine electromagnetic scattering parameter set is used as compressed data, so that the compression of the wind turbine RCS data can be realized, the wind turbine electromagnetic scattering parameter set is subjected to vector superposition, and the reconstruction of the wind turbine RCS data can also be realized. The invention verifies the feasibility of the method by introducing two evaluation parameters of data compression ratio and data reconstruction error of RCS data of the wind turbine. The data compression ratio of the RCS data of the wind turbine is defined as the ratio of the data quantity of the original RCS data of the wind turbine to the data quantity of the compressed data, the data reconstruction error of the RCS data of the wind turbine is defined as the difference between the original RCS data of the wind turbine and the reconstructed RCS data of the wind turbine, and an absolute value is obtained.
The theory basis of the compression and reconstruction of the wind turbine RCS data in the steps is a high-frequency electromagnetic scattering theory and a vector superposition theory, and a parameter set consisting of the electric field amplitude, the scattering coefficient and the coordinate parameter of the wind turbine electromagnetic scattering is adopted to represent a large amount of wind turbine RCS data, so that the effective compression and reconstruction of the wind turbine RCS data are realized. Specifically, as shown in fig. 1 and 2.
A wind turbine RCS data compression and reconstruction method comprises the following steps:
the method comprises the following steps: according to the angle schematic diagram of the wind motor and the radar sight shown in fig. 1 and the geometric model of the wind motor shown in fig. 2, determining the operating attitude angle variation range of the wind motor, and solving RCS data of the wind motor under each operating attitude, including amplitude information and phase information of the RCS data of the wind motor;
step two: according to the theory of high-frequency electromagnetic scattering, when a radar signal emits an electromagnetic wave with a frequency f in one direction to irradiate the wind turbine, the electromagnetic scattering of the wind turbine can be expressed as:
Figure BDA0001851204210000051
in the formula: e (m) is RCS data of the wind turbine; k is the dimension of the wind turbine electromagnetic scattering parameter set; a. theiα as strength parameter of electromagnetic scattering of wind turbineiFor electromagnetic scattering of wind turbinesA structure type parameter of (1); r isiPosition parameters of electromagnetic scattering of the wind turbine are obtained; f (m) is the step frequency, f0For the radar signal start frequency, f (m) ═ f0+ M · Δ f, where M is 0,1,2, …, M-1, where M is the number of frequency sampling points, M represents the mth frequency sampling point, and Δ f is the frequency sampling interval; c is the wave velocity of the electromagnetic wave; omega (m) is additive complex Gaussian white noise with mean 0 and variance sigma2。
And step three, because the power function and the exponential function component exist in the above formula at the same time, the parameter is difficult to solve, and therefore the above formula needs to be simplified. According to the Euler formula
Figure BDA0001851204210000052
And satisfies m.DELTA.f in consideration of the radar station actually operating<<f0And αi·Δf/f0<<1, the above equation can therefore be simplified to a form containing only an exponential function:
Figure BDA0001851204210000053
order to
Figure BDA0001851204210000054
Figure BDA0001851204210000055
The above formula can be abbreviated as:
Figure BDA0001851204210000056
further, an autocorrelation matrix and a cross-correlation matrix of the RCS data of the wind turbine generator are calculated, and the following vectors are constructed according to the RCS data of the wind turbine generator obtained in the step one:
X(m)＝[E(m)，E(m+1)，…，E(m+N-1)]T
Y(m)＝[E(m+1)，E(m+2)，…，E(m+N)]T
in the formula, N is an adjustable factor, and K is more than or equal to M/2.
And calculating an autocorrelation matrix and a cross-correlation matrix of the RCS data of the wind turbine according to the constructed column vectors:
RXX＝E[X(m)XH(m)]
RXY＝E[X(m)YH(m)]
in the formula, H represents a conjugate transpose of a matrix.
Further, since step one inevitably receives noise and other interference when calculating the wind turbine RCS data, a matrix R constructed from the wind turbine RCS data is createdXXAnd RXYAnd the ill-condition characteristic value exists, so that the solving result of the wind turbine electromagnetic scattering parameter set has deviation. To reduce the influence of noise and interference on the estimation result, the autocorrelation matrix R is first processedXXDecomposing the characteristic value of RXXIs taken as the variance sigma of Gaussian white noise2To construct a matrix C free from the influence of white Gaussian noiseXXAnd CXY：
CXX＝RXX-σ2I
CXY＝RXY-σ2Z
In the formula, I is an N-order identity matrix;
Figure BDA0001851204210000061
to further reduce the effect of noise and interference on the estimation result, the matrix C is again appliedXXSingular value decomposition is carried out, an NxN dimensional matrix with pathological eigenvalues is reduced to a KxK dimensional matrix without pathological eigenvalues, and main singular value matrixes sigma corresponding to K main singular values are stored1Main left singular vector matrix U1And a main right singular vector matrix V1. Matrix sigma1、U1And V1And (4) corresponding to the signal subspace after removing the noise and the interference twice.
Further, calculate U1 HCxyV1And solving the matrix bundle { ∑1，U1 HCxyV1The generalized eigenvalue of the parameter P is obtainediCan then solve the air-out motorScattering coefficient α of electromagnetic scatteringiAnd a coordinate parameter ri：
Figure BDA0001851204210000062
Figure BDA0001851204210000063
Electric field amplitude A for electromagnetic scattering of wind turbineiAccording to the RCS data of the wind turbine obtained in the step one and the solved emission and dispersion coefficients α of the electromagnetic dispersion of the wind turbineiAnd a coordinate parameter riAnd constructing a vector E and a vector A ', and then solving the electric field amplitude A of the electromagnetic scattering of the wind turbine according to the vector E and the vector A'.
Vectors E and A' are constructed as follows:
E＝[E(1)，E(2)，...，E(m)]T
A＝[A1，A2，…，AK]T
A′[a′(α1，r1)，a′(α2，r2)，…，a′(αK，rK)]
Figure BDA0001851204210000071
the solving formula of the electric field amplitude of the wind motor electromagnetic scattering is as follows:
A＝(A′HA′)-1A′HE
therefore, the electric field amplitude, the scattering coefficient and the coordinate parameter of the electromagnetic scattering of the wind turbine are solved.
Further, the solved wind turbine electromagnetic scattering parameter set { A }i，αi，riStoring the data as compressed data, namely compressing the RCS data of the wind turbine generator; set the electromagnetic scattering parameter of the wind turbine { A }i，αi，riThe reconstruction of RCS data of the wind motor can be realized by carrying out vector superposition, and the RCS data of the wind motor can be reconstructed by the wind motorThe formula for reconstructing the RCS of the wind turbine by the electromagnetic scattering parameter set is as follows:
Figure BDA0001851204210000072
in the formula, ErAnd (m) is the reconstructed wind turbine RCS data, and other parameters have the same meanings as the above.
Example (b):
according to the steps, a golden wind 77/1500 type wind turbine is taken as an example, a wind turbine geometric model with the same size is established to carry out wind turbine RCS data compression reconstruction example analysis. The geometric model of the wind turbine is shown in FIG. 2, the height of a tower is 85.0m, the length of a blade is 37.3m, the length of a cabin is 11.5m, and the rotation angle of the blade is 0 degree. The pitch angle of incident waves of the radar is 90 degrees, the polarization mode is vertical polarization, the initial frequency is 1GHz, the frequency bandwidth is 2GHz, the frequency step is 2MHz, and the total number of 1000 frequency sampling points is 1000, so that the original RCS data volume of the wind turbine is 3000. In order to further discuss the compression and reconstruction effect of the RCS data of the wind turbine generator, the calculation results of the traditional TDFT method are compared and analyzed. The comparison idea is that a proper threshold value is selected to ensure that the reconstruction result of the TDFT method and the reconstruction result of the wind turbine RCS data of the method are within the same error range, and on the basis, the data compression ratio of the two methods is compared. Analyzing the RCS data compression reconstruction effect of the wind turbine under different yaw angles, and the yaw angles of the wind turbine
Figure BDA0001851204210000081
The value range of (1) is usually 0-360 degrees, and the yaw angle is selected in the text
Figure BDA0001851204210000082
The analysis was performed for three cases of 0 °, 90 °, and 180 °.
When yaw angle
Figure BDA0001851204210000083
At 0 deg., fig. 3 is a simulation result of the method of the present invention, where fig. 3(a) is a reconstruction result of RCS data of the wind turbine, a solid line in the figure is an original RCS of the wind turbine, and a dotted line is a reconstructed RCS (to obtainThe same applies below), fig. 3(b) is a reconstruction error result. Fig. 4 is a simulation result of the conventional TDFT method, in which fig. 4(a) is a reconstruction result of the wind turbine RCS data, and fig. 4(b) is a reconstruction error result. Table 1 lists the compression results and reconstruction error statistics results of the original RCS data of the wind turbine by the method of the present invention and the conventional TDFT method. From Table 1, it can be seen that the yaw angle
Figure BDA0001851204210000084
When the error is 0 degree, the data compression ratio is 24.19, 912 reconstruction errors are smaller than 3dB, 77 reconstruction errors are between 3dB and 5dB, and 11 reconstruction errors are larger than 5 dB. For the traditional TDFT method, the data compression ratio is 13.33, 908 reconstruction errors are smaller than 3dB, 35 reconstruction errors are between 3dB and 5dB, and 57 reconstruction errors are larger than 5 dB.
TABLE 1
Figure BDA0001851204210000085
Time, compression reconstruction performance comparison of the two methods
Figure BDA0001851204210000086
When yaw angle
Figure BDA0001851204210000087
At 90 °, fig. 5 is a simulation result of the method of the present invention, where fig. 5(a) is a reconstruction result of the RCS data of the wind turbine, and fig. 5(b) is a reconstruction error result. Fig. 6 is a simulation result of the conventional TDFT method, in which fig. 6(a) is a reconstruction result of the wind turbine RCS data and fig. 6(b) is a reconstruction error result. Table 2 lists the compression results and the reconstruction error statistics results of the original RCS data of the wind turbine by the method of the present invention and the conventional TDFT method. From Table 2, it can be seen that the yaw angle
Figure BDA0001851204210000088
When the temperature is 90 degrees, the data compression ratio is 31.25, 847 reconstruction errors are smaller than 3dB and 126 reconstruction errors are between 3dB and 5dB for the method provided by the inventionThere are 27 with a difference of more than 5 dB. For the traditional TDFT method, the data compression ratio is 20.41, 843 reconstruction errors are smaller than 3dB, 79 reconstruction errors are between 3dB and 5dB, and 78 reconstruction errors are larger than 5 dB.
TABLE 2
Figure BDA0001851204210000091
Time, compression reconstruction performance comparison of the two methods
Figure BDA0001851204210000092
When yaw angle
Figure BDA0001851204210000093
At 180 °, fig. 7 shows a simulation result of the method of the present invention, where fig. 7(a) shows a reconstruction result of RCS data of the wind turbine, and fig. 7(b) shows a reconstruction error result. Fig. 8 is a simulation result of the conventional TDFT method, where fig. 8(a) is a result of reconstructing RCS data of the wind turbine and fig. 8(b) is a result of reconstructing errors. Table 3 lists the compression results and the reconstruction error statistics results of the original RCS data of the wind turbine by the method of the present invention and the conventional TDFT method. From Table 3, it can be seen that the yaw angle
Figure BDA0001851204210000094
At 180 deg., the data compression ratio is 22.73, 906 reconstruction errors are smaller than 3dB, 71 reconstruction errors are between 3dB and 5dB, and 23 reconstruction errors are larger than 5 dB. For the traditional TDFT method, the data compression ratio is 10.75, 901 reconstruction errors are smaller than 3dB, 44 reconstruction errors are between 3dB and 5dB, and 55 reconstruction errors are larger than 5 dB.
TABLE 3
Figure BDA0001851204210000095
Time, compression reconstruction performance comparison of the two methods
Figure BDA0001851204210000096
Through the analysis, the wind turbine RCS data compression and reconstruction method provided by the invention has a higher data compression ratio under the condition of ensuring the same reconstruction error, so that the method can save wind turbine RCS data storage resources in engineering practice and improve the real-time performance of wind turbine electromagnetic scattering characteristic analysis and wind turbine radar echo simulation on the radar side.

Claims (7)

1. A wind turbine RCS data compression reconstruction method is characterized by comprising the following steps: a parameter set formed by combining the electric field amplitude, the scattering coefficient and the coordinate parameter of the wind turbine electromagnetic scattering is used for representing a large amount of wind turbine RCS data, and effective compression and reconstruction of the wind turbine RCS data are achieved.
2. A wind turbine RCS data compression reconstruction method is characterized by comprising the following steps:
the method comprises the following steps: solving RCS data of the wind turbine under different frequency bands and attitude angle conditions, wherein the RCS data comprises amplitude and phase data of the RCS of the wind turbine;
step two: according to the RCS data of the wind turbine, calculating an autocorrelation matrix and a cross-correlation matrix of the RCS data of the wind turbine, and solving a conversion matrix between the two matrixes, so as to solve a parameter set formed by combining an electric field amplitude value, a scattering coefficient and a coordinate parameter of electromagnetic scattering of the wind turbine;
step three: the wind turbine electromagnetic scattering parameter set solved in the second step is used as compressed data, namely the compression of the wind turbine RCS data is realized; vector superposition is carried out on the wind turbine electromagnetic scattering parameter set, and reconstruction of wind turbine RCS data is achieved.
3. The method for compressing and reconstructing RCS data of the wind turbine according to claim 2, wherein the method comprises the following steps: in the first step, the operation attitude angle of the wind turbine comprises a radar view line pitch angle, a wind turbine yaw angle and a wind turbine blade rotation angle.
4. The method for compressing and reconstructing RCS data of the wind turbine according to claim 2, wherein the method comprises the following steps: in the second step, the calculated autocorrelation matrix and cross-correlation matrix are solved by constructing two column vectors according to the RCS data of the wind turbine generator, and a phase difference exists between the two column vectors.
5. The method for compressing and reconstructing RCS data of the wind turbine according to claim 2, wherein the method comprises the following steps: and in the third step, in order to explain the data compression and reconstruction effect of the wind turbine RCS, selecting a data compression ratio and a data reconstruction error as evaluation parameters.
6. The method for compressing and reconstructing RCS data of the wind turbine generator as claimed in any one of claims 2 to 5, wherein: the method is used for analyzing the electromagnetic interference characteristics of the radar station in real time by the wind turbine and simulating the radar echo of the wind turbine in real time.
7. A wind turbine RCS data compression reconstruction method is characterized by comprising the following steps:
the method comprises the following steps: determining the operating attitude angle change range of the wind motor according to the angle between the wind motor and the radar sight line and the wind motor geometric model, and solving RCS data of the wind motor under each operating attitude, including amplitude information and phase information of the RCS data of the wind motor;
step two: according to the theory of high-frequency electromagnetic scattering, when a radar signal emits electromagnetic waves with frequency f in one direction to irradiate the wind motor, the electromagnetic scattering of the wind motor is expressed as follows:
Figure FDA0001851204200000011
in the formula: e (m) is RCS data of the wind turbine; k is the dimension of the wind turbine electromagnetic scattering parameter set; a. theiα as strength parameter of electromagnetic scattering of wind turbineiThe structural type parameter of the electromagnetic scattering of the wind turbine is obtained; r isiPosition parameters of electromagnetic scattering of the wind turbine are obtained; f (m) is the step frequency, f0For the radar signal start frequency, f (m) ═ f0+ M · Δ f, M ═ 0,1, 2.., M-1, whereM is the number of frequency sampling points, M is the mth frequency sampling point, and delta f is the frequency sampling interval; c is the wave velocity of the electromagnetic wave; omega (m) is additive complex Gaussian white noise with mean 0 and variance sigma2；
Step three, according to the Euler formula
Figure FDA0001851204200000021
And considering that the radar station actually operating satisfies m.DELTA.f < f0And αi·Δf/f0Two conditions of < 1, so the above equation can be simplified to a form containing only exponential functions:
Figure FDA0001851204200000022
order to
Figure FDA0001851204200000023
The above formula can be abbreviated as:
Figure FDA0001851204200000024
further, an autocorrelation matrix and a cross-correlation matrix of the RCS data of the wind turbine generator are calculated, and the following vectors are constructed according to the RCS data of the wind turbine generator obtained in the step one:
X(m)＝[E(m)，E(m+1)，...，E(m+N-1)]T
Y(m)＝[E(m+1)，E(m+2)，...，E(m+N)]T
in the formula, N is an adjustable factor, and K is more than or equal to M/2;
and calculating an autocorrelation matrix and a cross-correlation matrix of the RCS data of the wind turbine according to the constructed column vectors:
RXX＝E[X(m)XH(m)]
RXY＝E[X(m)YH(m)]
in the formula, H represents a conjugate transpose of a matrix;
further, since step one, when calculating the wind turbine RCS data, is inevitably affected by noise and other disturbances,resulting in a matrix R constructed from the RCS data of the wind turbinesXXAnd RXYThe pathological characteristic value exists, so that the solving result of the wind turbine electromagnetic scattering parameter set has deviation; to reduce the influence of noise and interference on the estimation result, the autocorrelation matrix R is first processedXXDecomposing the characteristic value of RXXIs taken as the variance sigma of Gaussian white noise2To construct a matrix C free from the influence of white Gaussian noiseXXAnd CXY：
CXX＝RXX-σ2I
CXY＝RXY-σ2Z
In the formula, I is an N-order identity matrix;
Figure FDA0001851204200000031
to further reduce the effect of noise and interference on the estimation result, the matrix C is again appliedXXSingular value decomposition is carried out, an NxN dimensional matrix with pathological eigenvalues is reduced to a KxK dimensional matrix without pathological eigenvalues, and main singular value matrixes sigma corresponding to K main singular values are stored1Main left singular vector matrix U1And a main right singular vector matrix V1(ii) a Matrix sigma1、U1And V1Correspondingly removing the signal subspace after the noise and the interference are removed twice;
further, calculate U1 HCxyV1And solving the matrix bundle { ∑1,U1 HCxyV1The generalized eigenvalue of the parameter P is obtainediThe estimated value of (A) can be further solved, and the scattering coefficient α of the electromagnetic scattering of the air outlet motor can be further solvediAnd a coordinate parameter ri：
Figure FDA0001851204200000032
Figure FDA0001851204200000033
Electric field amplitude A for electromagnetic scattering of wind turbineiAccording to the RCS data of the wind turbine obtained in the step one and the solved emission and dispersion coefficients α of the electromagnetic dispersion of the wind turbineiAnd a coordinate parameter riConstructing a vector E and a vector A ', and then solving the electric field amplitude A of the electromagnetic scattering of the wind turbine by the vector E and the vector A';
vectors E and A' are constructed as follows:
E＝[E(1)，E(2)，…，E(m)]T
A＝[A1，A2，…，AK]T
A′＝[a′(α1，r1)，a′(α2，r2)，…，a′(αK，rK)]
Figure FDA0001851204200000041
the solving formula of the electric field amplitude of the wind motor electromagnetic scattering is as follows:
A＝(A'HA')-1A'HE
therefore, the electric field amplitude, the scattering coefficient and the coordinate parameter of the electromagnetic scattering of the wind turbine are solved;
further, the solved wind turbine electromagnetic scattering parameter set { A }i,αi,riStoring the data as compressed data, namely compressing the RCS data of the wind turbine generator; set the electromagnetic scattering parameter of the wind turbine { A }i,αi,riAnd (6) reconstructing the RCS data of the wind driven generator by vector superposition, wherein the formula for reconstructing the RCS of the wind driven generator through the wind driven generator electromagnetic scattering parameter set is as follows:
Figure FDA0001851204200000042
in the formula, ErAnd (m) is the reconstructed wind turbine RCS data.
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