Offshore wind turbine modal identification method based on mixed modal identification, medium and equipment

Abstract

The invention relates to the technical field of offshore wind energy, in particular to an offshore wind turbine modal identification method based on hybrid modal identification, a medium and equipment, and the method comprises the following steps: removing harmonic components in response by using a random subspace-Kalman filtering method; applying the response without the harmonic component to the improved natural excitation technology-characteristic system to realize modal identification of the algorithm; the response with the harmonic component removed is used for frequency identification of a power spectral density transmissibility method; comparing a power spectral density transmissibility method with an improved natural excitation technology-characteristic system to realize algorithm frequency results to determine correct structure frequency; and finally, correct structural physical modal information is extracted from an improved natural excitation technology-feature system implementation algorithm. The technical problem that an existing modal identification method is limited when being applied to the offshore wind turbine generator and cannot accurately identify the modal parameters of the offshore wind turbine generator is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of offshore wind energy, and in particular to an offshore wind turbine modal identification method, medium and equipment based on hybrid modal identification. Background Art

[0002] Wind energy is one of the fastest growing areas of renewable energy. As key facilities for offshore wind energy development, offshore wind turbines (OWTs) are vulnerable to damage due to exposure to harsh and complex marine environments. OWTs are continuously affected by environmental loads (such as wind and waves) during operation, and occasionally face extreme loads from events such as typhoons and earthquakes. In order to monitor the structural safety of OWTs and prevent catastrophic accidents caused by structural damage, vibration-based structural health monitoring (SHM) is generally considered to be one of the most effective strategies. Wind energy industry companies also hope to ensure the safe operation of OWTs by using structural health monitoring (SHM) strategies. In the SHM of OWTs, modal identification plays a vital role because the identified modal parameters can provide important information for further structural state assessment and damage detection. Existing methods are usually developed under the assumption that the structure is excited by Gaussian white noise. However, during the operation of OWTs, the structure is often affected by environmental factors such as waves and wind, and the high-speed rotating blades also continuously affect the dynamic behavior of the structure. At this time, the excitation of OWTs will become more complicated. In addition to white noise, there is a high possibility of colored noise and harmonic excitation. The above excitation conditions obviously violate the assumption of Gaussian white noise, which limits the application of traditional modal identification methods to OWTs and makes it challenging to accurately identify their modal parameters. Summary of the invention

[0003] The present invention aims to provide an offshore wind turbine modal identification method, medium and device based on hybrid modal identification to solve the technical problem that the modal identification method in the prior art is limited when applied to offshore wind turbines and cannot accurately identify their modal parameters. The specific technical solution is as follows: The present invention provides an offshore wind turbine modal identification method based on hybrid modal identification, comprising the following steps: S1. Obtain the acceleration vibration response of the target structure through the sensor of the measurement platform; S2, determining the harmonic frequency of the acceleration vibration response in S1, and then using the random subspace-Kalman filter method to remove the harmonic components in the response to obtain the acceleration response with the harmonic components removed; S3. The acceleration response with harmonic components removed is used for modal identification of the improved natural excitation technology-characteristic system implementation algorithm to obtain the modal frequency results of the improved natural excitation technology-characteristic system implementation algorithm; the acceleration vibration response with harmonic components removed is used for frequency identification of the power spectral density transfer rate method to obtain the structural frequency after colored noise is removed; S4. Compare the modal frequency results of the improved natural excitation technology-characteristic system implementation algorithm with the structural frequency after removing the colored noise to determine the correct structural frequency; S5. Based on the correct structural frequency obtained in S4, the correct structural physical modal information is extracted from the improved natural excitation technology-characteristic system implementation algorithm to obtain the correct modal parameters of the fan.

[0004] The further improvement of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention is that S2 specifically includes: S201, determining the acceleration vibration response and its harmonic frequency magnitude; S202, obtain the state matrix by random subspace method based on acceleration vibration response , observation matrix And the three covariance matrices , and ; S203, using the acceleration vibration response, the determined harmonic frequency magnitude, the state matrix, the observation matrix and the three covariance matrices to obtain the harmonic component response through the Kalman filtering method; S204, decomposing the harmonic component response by using a spatial orthogonal projection and matrix decomposition method to obtain an acceleration response with the harmonic component removed.

[0005] The further improvement of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention is that the state matrix and the observation matrix The calculation steps include: Assume the acceleration vibration response as the observed data , based on observational data Construct the Hank matrix for past time steps and the Hank matrix for future time steps , thus we get the projection matrix for: 1); in, , represents the number of rows of the defined Hank matrix block, Indicates that there is Number of block rows, Indicates that there is Number of block rows, is the number of block columns, represents the length of the observation sequence, represents the Hank matrix for future time steps, Represents the projection matrix From the currently defined equation, we get is the number of sensors; Will Perform singular value decomposition to obtain: 2); in, represents the transpose of a matrix, represents the decomposed left matrix, represents the decomposed right matrix, represents a diagonal matrix of singular values; Based on formula 2), we can further obtain the extended observability matrix: 3); in, and express and forward List, Indicates the order of selection; express Remove the last block The extended observability matrix of rows; followed by the state sequence From formula 4), we get: 4); in, Indicates The state sequence of the block matrix block row number, Indicates The state sequence of the block matrix block row number, express The Moore-Penrose pseudoinverse, express Remove the last The Moore-Penrose pseudoinverse of the line; matrix and From formula 5), ​​we get: 5); in, Represents a Hank matrix with only one row of blocks.

[0006] The further improvement of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention is that the covariance matrix , and From the residual we get: 6); in, represents the transpose of a matrix, Express expectations, and are Kalman filter residuals.

[0007] A further improvement of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention is that S203 specifically includes the following steps: Based on Kalman filtering and system state equation estimation, the state sequence of each order is obtained, and the optimal state estimation is as follows: 7); in, represents the optimal estimate for the next time step, represents the state of the current time step, represents the state matrix, represents Kalman filtering, and defines From formula 8) we get: The observation equation is expressed as: 8); in, represents the observed data, represents the observation matrix; Kalman Gain The update is expressed as: 9); in, represents the covariance matrix of the Kalman filter; The update of the covariance matrix is ​​expressed as: 10); in, Represents the estimated covariance matrix of the Kalman filter for the next time step; The estimated state and matrix Based on a reversible matrix Performing linear transformation yields: 11); in, Representation based on the reversible matrix The linear change of represents the inverse of a matrix; This matrix is ​​defined as a modal basis to distinguish the modal and harmonic components of the system and is constructed as follows: 12); in, represents the real part, represents the imaginary part, Representation Matrix The eigenvector of Define a position matrix , used to locate the harmonic components: 13); in, represents the order number of the system mode, represents the number of harmonic modes, represents the identity matrix; Based on Equation 14) and Equation 16), the harmonic component response Calculated by the following formula: 14); 15); in, is the number of sensors.

[0008] The further improvement of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention is that the improved natural excitation technology-characteristic system implementation algorithm method in S3 is specifically: Firstly, the acceleration response with harmonic components removed is used as the input of the improved natural excitation technology-characteristic system implementation algorithm method; Then, the key parameter values ​​of the natural excitation technology-characteristic system implementation algorithm are obtained based on the dual reference point Monte Carlo stability diagram; Then, a preliminary result set was obtained based on the double reference point Monte Carlo stability diagram and fuzzy clustering method; Finally, the modal parameter results of the characteristic system implementation algorithm are obtained.

[0009] The further improvement of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention is that the power spectral density transfer rate method in S3 specifically includes the following steps: The transmissibility function is a function of two dynamic responses and The power spectrum transfer rate is is relative to the reference response and , The ratio of the cross power spectral density is as follows: 16); in, Indicates response and The cross-correlation power spectral density is Indicates response and The cross-correlation power spectral density, the ratio of the cross-power spectral density makes the natural frequency At , the forced vibration term is eliminated, and finally converges to the ratio of the vibration mode amplitudes: 17); So the power spectral density transfer ratio method (at the natural frequency) for the applied excitation and the reference response is It is independent. Indicates that the frequency is approximately equal to the natural frequency, and All represent vibration mode amplitudes; When The power spectrum density transfer rate method of the response is measured at each position, and the power spectrum density transfer rate method matrix is ​​constructed: 18); in, Indicates response and The cross-correlation power spectral density is Indicates response and The cross-correlation power spectral density of the power spectral density transfer rate method has a unique property at the natural frequency, that is, the rank is 1, and its columns are linearly correlated, so the modal frequency is identified by performing singular value decomposition on the matrix.

[0010] A further improvement of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention is that, in order to avoid the occurrence of false modes, the power spectrum density transfer rate method matrix is ​​reconstructed, and the reconstructed power spectrum density transfer rate method matrix is ​​as follows: 19); in, Representation Matrix No. singular values, Representation Matrix No. right singular vectors, Representation Matrix No. left singular vectors, represents the number of singular values ​​to be summed; by constructing The reconstructed power spectral density transfer rate method matrix is ​​used, and singular value decomposition is performed, the first singular value is taken, and finally the weighted average is performed to obtain the weighted average function : 20); in, Representation Matrix The first singular value of ; Finally, the structural physical modal information of the system is screened out from the characteristic system implementation algorithm results according to the weighted average function of formula 20).

[0011] The present invention also includes a readable storage medium, wherein the readable storage medium stores a computer program, wherein the computer program is suitable for being loaded by a processor and executing the offshore wind turbine modal identification method based on hybrid modal identification as described above.

[0012] The present invention also includes a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the offshore wind turbine modal identification method based on hybrid modal identification as described above is run.

[0013] The application of the technical solution of the present invention has the following beneficial effects: The present invention is based on a hybrid modal identification method for offshore wind turbines, which obtains measured acceleration responses; determines harmonic frequencies and extracts harmonic component responses through a random subspace-Kalman filter method; obtains acceleration responses with harmonic components removed through orthogonal projection and LQ decomposition; then applies the improved natural excitation technology-characteristic system implementation algorithm to obtain modal parameter results; the acceleration response with harmonic components removed is also simultaneously applied to a power spectral density transfer rate method to eliminate the influence of colored noise; finally, the correct structural physical mode is screened from the improved natural excitation technology-characteristic system implementation algorithm based on the modal frequency results of the power spectral density transfer rate method. The method of the present invention first removes the influence of harmonic components on modal identification by using a random subspace-Kalman filter method, and then eliminates the influence of colored noise on modal identification according to the power spectral density transfer rate method, and finally screens out the correct modal parameters from the results of the improved natural excitation technology-characteristic system implementation algorithm. Compared with a single method, this hybrid modal identification framework has strong adaptability and effectiveness in modal identification of offshore wind turbines under complex excitations, solving the technical problem that the modal identification methods in the prior art are limited when applied to offshore wind turbines and cannot accurately identify their modal parameters.

[0014] In addition to the above-described purposes, features and advantages, the present invention has other purposes, features and advantages. The present invention will be further described in detail with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The drawings constituting a part of this application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings: Figure 1is a flow chart of an offshore wind turbine modal identification method based on hybrid modal identification according to the present invention; Figure 2 It is a power spectrum diagram of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention before removing the harmonic components; Figure 3 It is a power spectrum diagram after removing harmonic components of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention; Figure 4 It is a result schematic diagram of TMC-NExT-ERA of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention; Figure 5 It is a schematic diagram of the result of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention after PSDT; Figure 6 It is a schematic diagram of the TMC-NExT-ERA results of the offshore wind turbine modal identification method based on hybrid modal identification of the present invention after PSDT screening. DETAILED DESCRIPTION

[0016] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0017] See also Figure 1 As shown, a method for modal identification of offshore wind turbines based on hybrid modal identification includes the following steps: S1. Obtain the acceleration vibration response of the target structure through the sensor of the measurement platform; S2, determine the harmonic frequency of the acceleration vibration response in S1, and then use SI-KF (random subspace-Kalman filter method) to remove the harmonic components in the response to obtain the acceleration response with the harmonic components removed, such as Figure 2 and Figure 3 As shown; S3. The acceleration response with harmonic components removed is used for modal identification of TMC-NExT-ERA (improved natural excitation technology-characteristic system implementation algorithm) to obtain the modal frequency results of TMC-NExT-ERA, such as Figure 4 As shown; the acceleration vibration response with harmonic components removed is applied to the frequency identification of PSDT (power spectral density transfer rate method) to obtain the structural frequency after removing the colored noise, as shown in Figure 5 As shown; S4. Compare the modal frequency results of TMC-NExT-ERA with the structural frequency after removing the colored noise to determine the correct structural frequency; S5. Based on the correct structural frequency obtained in S4, the correct structural physical modal information is extracted from TMC-NExT-ERA to obtain the correct modal parameters of the fan, such as Figure 6 shown.

[0018] like Figure 1 As shown in the figure, the specific process is as follows: the acceleration vibration response of the structural response is subjected to power spectrum analysis and harmonic frequency is determined; the harmonic components are removed by SI-KF and the system response is retained; the dual reference point Monte Carlo stability diagram is obtained by modal identification of TMC-NExT-ERA; meanwhile, the average weight function is obtained by frequency identification of PSDT; finally, the modal parameters are obtained by the dual reference point Monte Carlo stability diagram and the average weight function.

[0019] Figure 2 and Figure 3 In the figure, PSD (power spectral density) is the ordinate and frequency is the abscissa. The blue curve and the red curve are the power spectral densities of the two acceleration responses in the orthogonal directions of the fifth degree of freedom. Figure 2 The sky blue area is the harmonic component, and the light orange area is the colored noise component. Figure 2 The blue area in the sky is the harmonic component. Figure 3 The red vertical line in the middle indicates the state where harmonics have been removed. Figure 4 In the figure, the number of calculations is the ordinate on the left, PSD (power spectral density) is the ordinate on the right, and the frequency is the abscissa. The blue curve and the red curve are the power spectral densities of the two acceleration responses in the orthogonal directions of the fifth degree of freedom in the wind turbine. The red dots are stable results, the light pink dots are unstable false results, the light orange vertical area is the colored noise component, the orange horizontal area is the area where the number of calculations is less than 50, and the light blue part is the area where the number of calculations is greater than 50. Figure 5 In the figure, the four blue dots are the first four frequencies determined, and the red vertical line is the state where the harmonics have been removed. At this time, the colored noise components in the light orange area are eliminated. Figure 6 In the figure, the damping ratio is the ordinate, the frequency is the abscissa, the blue circle is the ERA first-order mode cluster set, the orange circle is the ERA second-order mode cluster set, the yellow circle is the ERA third-order mode cluster set, the purple circle is the ERA fourth-order mode cluster set, and the green circle is the ERA fifth-order mode cluster set.

[0020] Preferably, S2 specifically includes: S201, determining the acceleration vibration response and its harmonic frequency magnitude; S202, obtain the state matrix by random subspace method based on acceleration vibration response , observation matrix And the three covariance matrices , and ; S203, using the acceleration vibration response, the determined harmonic frequency magnitude, the state matrix, the observation matrix and the three covariance matrices to obtain the harmonic component response through the Kalman filtering method; S204, decomposing the harmonic component response by using a spatial orthogonal projection and matrix decomposition method to obtain an acceleration response with the harmonic component removed.

[0021] Preferably, the state matrix and the observation matrix The calculation steps include: Assume the acceleration vibration response as the observed data , based on observational data The Hankel matrix of past time steps can be constructed and the Hank matrix for future time steps , from which we can get the projection matrix for: 1); in, , represents the number of rows of the defined Hank matrix block, Indicates that there is Number of block rows, Indicates that there is Number of block rows, is the number of block columns, represents the length of the observation sequence, represents the Hank matrix for future time steps, Represents the projection matrix From the currently defined equation, we get is the number of sensors; Will Perform singular value decomposition to obtain: 2); in, represents the transpose of a matrix, represents the decomposed left matrix, represents the decomposed right matrix, represents a diagonal matrix of singular values; Based on formula 2), we can further obtain the extended observability matrix: 3); in, and express and forward List, Indicates the order of selection; express Remove the last block The extended observability matrix of rows; followed by the state sequence From formula 4), we get: 4); in, Indicates The state sequence of the block matrix block row number, Indicates The state sequence of the block matrix block row number, express The Moore-Penrose pseudoinverse, express Remove the last The Moore-Penrose pseudoinverse of the line; matrix and It can be obtained from formula 5): 5); in, Represents a Hank matrix with only one row of blocks.

[0022] Preferably, the covariance matrix , and From the residual we can get: 6); in, represents the transpose of a matrix, Express expectations, and are Kalman filter residuals.

[0023] Preferably, S203 specifically includes the following steps: Based on Kalman filtering and system state equation estimation, the state sequence of each order is obtained, and the optimal state estimation is as follows: 7); in, represents the optimal estimate for the next time step, represents the state of the current time step, represents the state matrix, represents Kalman filtering, and defines From formula 8) we get: The observation equation can be expressed as: 8); in, represents the observed data, represents the observation matrix; Kalman Gain The update can be expressed as: 9); in, represents the covariance matrix of the Kalman filter; The update of the covariance matrix can be expressed as: 10); in, Represents the estimated covariance matrix of the Kalman filter for the next time step; The estimated state and matrix Based on a reversible matrix Performing linear transformation yields: 11); in, Representation based on the reversible matrix The linear change of represents the inverse of a matrix; This matrix is ​​defined as a modal basis to distinguish the modal and harmonic components of the system and is constructed as follows: 12); in, represents the real part, represents the imaginary part, Representation Matrix The eigenvector of Define a position matrix s for the location of harmonic components: 13); in, represents the order number of the system mode, represents the number of harmonic modes, represents the identity matrix; Based on Equation 11) and Equation 13), the harmonic component response Calculated by the following formula: 14); 15); in, is the number of sensors.

[0024] Preferably, the TMC-NExT-ERA method in S3 is as follows: first, the acceleration response with harmonic components removed is used as the input of the TMC-NExT-ERA method. Then, the key parameter values ​​of NExT-ERA (Natural Excitation Technology-Eigensystem Implementation Algorithm) are obtained according to the double reference point Monte Carlo stability diagram. Then, a result set is preliminarily obtained based on the double reference point Monte Carlo stability diagram and the fuzzy C-means clustering method (fuzzy clustering method). Finally, the modal parameter results of ERA (Eigensystem Implementation Algorithm) are obtained.

[0025] Preferably, the PSDT method in S3 specifically includes the following steps: The transmissibility function is a function of two dynamic responses and The power spectrum transfer rate is is relative to the reference response and , The ratio of the cross power spectral density is as follows: 16); in, Indicates response and The cross-correlation power spectral density is Indicates response and The cross-correlation power spectral density, the ratio of the cross-power spectral density makes the natural frequency At , the forced vibration term is eliminated, and finally converges to the ratio of the vibration mode amplitudes: 17); So the PSDT (at the natural frequency) for the applied excitation and the reference response It is independent. Indicates that the frequency is approximately equal to the natural frequency, and All represent vibration mode amplitudes; When The corresponding PSDT is obtained by measuring the position, and then the PSDT matrix is ​​constructed: 18); in, Indicates response and The cross-correlation power spectral density is Indicates response and The cross-correlation power spectral density of ; The PSDT matrix has a unique property at the natural frequency, that is, the rank is 1, and its columns are linearly correlated, so the modal frequency is identified by performing singular value decomposition on the matrix.

[0026] Preferably, in order to avoid false modes, the PSDT matrix is ​​reconstructed, and the reconstructed PSDT matrix is ​​as follows: 19); in, Representation Matrix No. singular values, Representation Matrix No. right singular vectors, Representation Matrix No. left singular vectors, represents the number of singular values ​​to be summed; by constructing Reconstructed PSDT matrix, and perform singular value decomposition, take the first singular value, and finally perform weighted average to obtain the weighted average function : 20); in, Representation Matrix The first singular value of ; Finally, the structural physical modal information of the system is filtered out from the ERA results according to the weighted average function of formula 20), and the correct modal parameters of the wind turbine are obtained.

[0027] The present invention also includes a readable storage medium, wherein the readable storage medium stores a computer program, wherein the computer program is suitable for being loaded by a processor and executing the offshore wind turbine modal identification method based on hybrid modal identification as described above.

[0028] The present invention also includes a computer device, which includes a memory and a processor. The memory stores a computer program. When the computer program is executed by the processor, the offshore wind turbine modal identification method based on hybrid modal identification as described above is run.

[0029] The present invention is based on a hybrid modal identification method for offshore wind turbines, which obtains measured acceleration response; determines harmonic frequency and extracts harmonic component response by SI-KF method; obtains acceleration response with harmonic component removed by orthogonal projection and LQ decomposition; then applies to TMC-NExT-ERA to obtain modal parameter results; applies the acceleration response with harmonic component removed to PSDT method to eliminate the influence of colored noise; finally, the correct structural physical mode is screened from TMC-NExT-ERA according to the frequency result of PSDT. The method of the present invention removes the influence of harmonic component on modal identification by using SI-KF method, and then eliminates the influence of colored noise on modal identification according to PSDT method, and finally screens out the correct modal parameters from the results of TMC-NExT-ERA. Compared with a single method, the hybrid modal identification framework has strong adaptability and effectiveness for modal identification of offshore wind turbines under complex excitation, and solves the technical problem that the modal identification method in the prior art is limited when applied to offshore wind turbines and cannot accurately identify its modal parameters.

[0030] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for modal identification of offshore wind turbines based on hybrid modal identification, characterized in that: The steps include: S1. Obtain the acceleration vibration response of the target structure through the sensor of the measurement platform; S2, determining the harmonic frequency of the acceleration vibration response in S1, and then using the random subspace-Kalman filter method to remove the harmonic components in the response to obtain the acceleration response with the harmonic components removed; S3. The acceleration response with harmonic components removed is used for modal identification of the improved natural excitation technology-characteristic system implementation algorithm to obtain the modal frequency results of the improved natural excitation technology-characteristic system implementation algorithm; the acceleration vibration response with harmonic components removed is used for frequency identification of the power spectral density transfer rate method to obtain the structural frequency after colored noise is removed; S4. Compare the modal frequency results of the improved natural excitation technology-characteristic system implementation algorithm with the structural frequency after removing the colored noise to determine the correct structural frequency; S5. Based on the correct structural frequency obtained in S4, the correct structural physical modal information is extracted from the improved natural excitation technology-characteristic system implementation algorithm to obtain the correct modal parameters of the fan.

2. The offshore wind turbine modal identification method based on hybrid modal identification according to claim 1 is characterized in that: S2 specifically includes: S201, determining the acceleration vibration response and its harmonic frequency magnitude; S202, obtain the state matrix by random subspace method based on acceleration vibration response , observation matrix And the three covariance matrices , and ; S203, using the acceleration vibration response, the determined harmonic frequency magnitude, the state matrix, the observation matrix and the three covariance matrices to obtain the harmonic component response through the Kalman filtering method; S204, decomposing the harmonic component response by using a spatial orthogonal projection and matrix decomposition method to obtain an acceleration response with the harmonic component removed.

3. The offshore wind turbine modal identification method based on hybrid modal identification according to claim 2 is characterized in that: State Matrix and the observation matrix The calculation steps include: Assume the acceleration vibration response as the observed data , based on observational data Construct the Hank matrix for past time steps and the Hank matrix for future time steps , thus we get the projection matrix for: 1）； in, , represents the number of rows of the defined Hank matrix block, Indicates that there is Number of block rows, Indicates that there is Number of block rows, Indicates the number of block columns, represents the length of the observation sequence, Represents the projection matrix From the currently defined equation, we get is the number of sensors; Will Perform singular value decomposition to obtain: 2）； in, represents the transpose of a matrix, represents the decomposed left matrix, represents the decomposed right matrix, represents a singular value diagonal matrix; Based on formula 2), we can further obtain the extended observability matrix: 3）； in, and express and forward List, Indicates the order of selection; express Remove the last The extended observability matrix of rows; followed by the state sequence From formula 4), we get: 4）； in, Indicates The state sequence of the block matrix block row number, It means The state sequence of the block matrix block row number, express The Moore-Penrose pseudoinverse, express Remove the last The Moore-Penrose pseudoinverse of the line; matrix and From formula 5), ​​we get: 5）； in, Represents a Hank matrix with only one row of blocks.

4. The offshore wind turbine modal identification method based on hybrid modal identification according to claim 3 is characterized in that: Covariance matrix , and From the residual we get: 6）； in, Express expectations, and Both represent Kalman filter residuals.

5. The offshore wind turbine modal identification method based on hybrid modal identification according to claim 2 is characterized in that: S203 specifically includes the following steps: Based on Kalman filtering and system state equation estimation, the state sequence of each order is obtained, and the optimal state estimation is as follows: 7）； in, represents the optimal estimate for the next time step, represents the state of the current time step, represents the state matrix, represents Kalman filtering, and defines From formula 8) we get: The observation equation is expressed as: 8）； in, represents the observation matrix; Kalman Gain The update is expressed as: 9）； in, represents the covariance matrix of the Kalman filter; The update of the covariance matrix is ​​expressed as: 10）； in, Represents the estimated covariance matrix of the Kalman filter for the next time step; The estimated state and matrix Based on a reversible matrix Performing linear transformation yields: 11）； in, Representation based on the reversible matrix The linear change of represents the inverse of a matrix; This matrix is ​​defined as a modal basis to distinguish the modal and harmonic components of the system and is constructed as follows: 12）； in, represents the real part, represents the imaginary part, Representation Matrix The eigenvector of Define a position matrix , used to locate the harmonic components: 13）； in, represents the order number of the system mode, represents the number of harmonic modes, represents the identity matrix; Based on Equation 11) and Equation 13), the harmonic component response Calculated by the following formula: 14）； 15）。 6. The offshore wind turbine modal identification method based on hybrid modal identification according to claim 1, characterized in that: The improved natural excitation technology in S3 - the characteristic system implementation algorithm is specifically as follows: Firstly, the acceleration response with harmonic components removed is used as the input of the improved natural excitation technology-characteristic system implementation algorithm method; Then, the key parameter values ​​of the natural excitation technology-characteristic system implementation algorithm are obtained based on the dual reference point Monte Carlo stability diagram; Then, a preliminary result set was obtained based on the double reference point Monte Carlo stability diagram and fuzzy clustering algorithm; Finally, the modal parameter results of the characteristic system implementation algorithm are obtained.

7. The offshore wind turbine modal identification method based on hybrid modal identification according to claim 1, characterized in that: The power spectral density transfer rate method in S3 specifically includes the following steps: The transmissibility function is a function of two dynamic responses and The power spectrum transfer rate is is relative to the reference response and , The ratio of the cross power spectral density is as follows: 16）； in, Indicates response and The cross-correlation power spectral density is Indicates response and The cross-correlation power spectral density, the ratio of the cross-power spectral density makes the natural frequency At , the forced vibration term is eliminated, and finally converges to the ratio of the vibration mode amplitudes: 17）； Therefore, the power spectral density transfer rate method is used for the applied excitation and the reference response. It is independent. Indicates that the frequency is approximately equal to the natural frequency, and All represent vibration mode amplitudes; When The power spectrum density transfer rate method of the response is measured at each position, and the power spectrum density transfer rate method matrix is ​​constructed: 18）； in, Indicates response and The cross-correlation power spectral density is Indicates response and The cross-correlation power spectral density of the power spectral density transfer rate method matrix has a unique property at the natural frequency, that is, the rank is 1, and its columns are linearly correlated, so the modal frequency is identified by performing singular value decomposition on the matrix.

8. The offshore wind turbine modal identification method based on hybrid modal identification according to claim 7 is characterized in that: In order to avoid the occurrence of false modes, the power spectrum density transfer rate method matrix is ​​reconstructed. The reconstructed power spectrum density transfer rate method matrix is ​​shown as follows: 19）； in, Representation Matrix No. singular values, Representation Matrix No. right singular vectors, Representation Matrix No. left singular vectors, represents the number of singular values ​​to be summed; by constructing The reconstructed power spectral density transfer rate method matrix is ​​used, and singular value decomposition is performed, the first singular value is taken, and finally the weighted average is performed to obtain the weighted average function : 20）； in, Representation Matrix The first singular value of ; Finally, the structural physical modal information of the system is screened out from the characteristic system implementation algorithm results according to the weighted average function of formula 20).

9. A readable storage medium, characterized in that: The readable storage medium stores a computer program, and the computer program is suitable for being loaded by a processor and executing the offshore wind turbine modal identification method based on hybrid modal identification according to any one of claims 1 to 8.

10. A computer device, characterized in that: The computer device includes a memory and a processor, the memory stores a computer program, and when the computer program is executed by the processor, the offshore wind turbine modal identification method based on hybrid modal identification according to any one of claims 1 to 8 is run.

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