A method for long-term tracking and identification of key structural dynamic characteristics of wind turbines

Abstract

This invention discloses a method for long-term tracking and identification of the dynamic characteristics of key structures in wind turbines. The method includes: acquiring acceleration vibration data and generator shaft speed data during long-term operation of the wind turbine; grouping the acquired acceleration vibration data and generator shaft speed data; determining the operating state of the wind turbine corresponding to each group of data based on the generator shaft speed signal; and independently plotting the relevant frequency and modal damping time history diagrams for different key components of the wind turbine according to the time history, thereby achieving long-term tracking and identification of the dynamic characteristics of the key structures in the wind turbine. This invention enables periodic online modal parameter identification and analysis of the key structures of wind turbines, thereby achieving long-term tracking and monitoring of the dynamic characteristics of the key structures.

Description

Technical Field

[0001] This invention relates to the field of structural monitoring technology, and in particular to a method for long-term tracking and identification of the dynamic characteristics of key structures in wind turbines. Background Technology

[0002] During operation, wind turbines are subjected to complex and variable environmental factors over a long period, leading to cumulative failures in critical structures such as towers and blades, including wear, fatigue, and cracks. If these problems are not detected and addressed promptly, they can range from affecting normal equipment operation to causing serious accidents. Therefore, research on wind turbine structural health monitoring technology is of great significance. The dynamic characteristics of a structure are crucial indicators for assessing its vibration performance and health status. Specifically, changes in structural modal frequencies over time reflect the structure's health condition, while changes in modal damping reflect the form of vibration. To better understand the health status and performance evolution of critical wind turbine structures during service, and to monitor abnormal vibrations and intervene promptly, it is essential to track and analyze the long-term dynamic characteristics of key wind turbine structures, including modal frequencies and modal damping information.

[0003] With increasing emphasis on structural health monitoring of wind turbines, many wind turbines now employ monitoring and control systems and data acquisition systems. These systems enable real-time monitoring of structural vibration response and environmental data, and the massive monitoring databases provide a foundation for studying the long-term dynamic characteristics of key wind turbine structures. Therefore, there is an urgent need for a method for extracting and tracking the dynamic characteristics of long-term monitoring signals from wind turbines. Summary of the Invention

[0004] In view of this, the present invention provides a method for long-term tracking and identification of the key structural dynamic characteristics of wind turbines.

[0005] This invention discloses a long-term tracking and identification method for the key structural dynamic characteristics of wind turbines, comprising:

[0006] Step 1: Obtain acceleration vibration data and generator shaft speed data during long-term operation of the wind turbine, and group the obtained acceleration vibration data and generator shaft speed data.

[0007] Step 2: Determine the operating status of the wind turbine corresponding to each set of data based on the generator shaft speed signal;

[0008] Step 3: Based on the time history, independently draw the relevant frequency and modal damping time history diagrams for different key components of the wind turbine to achieve long-term tracking and identification of the key structural dynamic characteristics of the wind turbine.

[0009] Further, step 2 includes:

[0010] Step 21: When the wind turbine is in operation, the vibration acceleration signal is synchronously processed using synchronous information processing technology; interference information related to shaft operation is removed from the data obtained after synchronous processing using a bandpass filter; the original sampling clock is used to resample the data after interference removal to restore the signal; and the peak picking method is used to obtain the modal frequency information of each component in the signal spectrum of each data group.

[0011] Further, step 21 includes:

[0012] Step 211: Based on the motor shaft speed data, process the vibration acceleration signal using the synchronization information processing method to obtain the order spectrum of the synchronization order signal in the periodic domain.

[0013] Step 212: For the order spectrum signal of the vibration acceleration signal, filter the synchronization order signal in the periodic domain and remove the order responses related to the rotating shaft to obtain the synchronization signal with the interference of the running response removed.

[0014] Step 213: Using resampling in synchronous information processing technology, the acceleration vibration is reverse-processed according to the timing sampling sequence of the original vibration acceleration signal to restore the time domain signal of the original vibration acceleration signal after removing the interference of the running response.

[0015] Step 214: Perform spectrum analysis on the time-domain signal, extract information from the spectrum using the peak picking method, and read the coordinates corresponding to the peak points to obtain the modal frequencies of each key structure.

[0016] Further, step 211 includes:

[0017] Instantaneous phase extraction is performed on the motor shaft speed data. A synchronous acquisition clock with an integer period is generated by inverse function transformation. The vibration acceleration signal is then resampled using the synchronous acquisition clock to obtain the synchronous order signal. The order spectrum is obtained by performing Fourier transform on the synchronous order signal.

[0018] Further, step 212 includes:

[0019] Step 2121: For the order spectrum signal of the vibration acceleration signal, use a bandpass filter to filter the synchronous order signal in the periodic domain to obtain multiple synchronous integer order signals related to the rotating shaft; remove the order response related to the rotating shaft from the order spectrum signal, and perform bandpass filtering on the order spectrum signal after removing the order response.

[0020] Step 2122: Remove the bandpass filtered signal from the original vibration acceleration signal to obtain the synchronization signal with the interference removed from the running response.

[0021] Further, step 2 includes:

[0022] Step 22: When the wind turbine is in a shutdown state, bandpass filtering is performed on the frequency range of energy concentration of the multi-mode. The random decrement method is used to convert the response acceleration signal induced by random environmental excitation of the multi-mode into a free decay signal. The damping information of different order mode components of the wind turbine structure in each set of data is obtained by fitting the exponential function model.

[0023] Further, step 22 includes:

[0024] Step 221: Bandpass filter the vibration acceleration signal to obtain single-mode acceleration data of the key structure of the wind turbine.

[0025] Step 222: Convert the single-mode acceleration data into a free decay signal using the random decrement method;

[0026] Step 223: Perform a Hilbert transformation on the free decay signal, take the logarithm of the transformed amplitude, and obtain the modal damping by fitting.

[0027] Furthermore, after step 2 and before step 3, the following steps are also included:

[0028] Repeat step 2 to obtain the multi-order frequencies and modal damping of the wind turbine generator under each time number corresponding to the grouped data.

[0029] Further, step 1 includes:

[0030] Acceleration vibration data and generator shaft speed data of wind turbines during long-term service are obtained from the wind turbine monitoring and control and data acquisition system; at the same time, the obtained acceleration vibration data and generator shaft speed data are grouped and processed according to time.

[0031] Because of the adoption of the above technical solution, the present invention has the following advantages:

[0032] 1. This invention is applicable to long-term tracking of the dynamic characteristics of key structures in wind turbines. It effectively considers the impact of data under different operating conditions and employs different analysis methods for processing and analyzing data under different conditions. The operating conditions of the wind turbine data are determined based on the shaft speed signal. For data under operating conditions, vibration acceleration and rotational speed signals are processed using synchronous information processing technology, and then spectral analysis combined with peak picking methods is used to obtain the modal frequencies of each key structure. For vibration signals under shutdown conditions, a bandpass filter is used to obtain the structural single-mode component signals caused by environmental excitation, and then a random decrement method is used to extract the free decay signal from the single-mode component for fitting damping.

[0033] 2. This invention acquires acceleration signals from the monitoring, control, and data acquisition system of a wind turbine and identifies multiple order modal frequencies and damping of key structures such as the tower and blades of the wind turbine. Case studies demonstrate that it exhibits significant reliability and practicality in terms of modal parameter identification accuracy and automation level.

[0034] 3. By tracking and identifying the long-term dynamic characteristics of key structures of wind turbines using the method of this invention, technical support can be provided for studying the evolution of the frequency and damping of key structures of wind turbines with respect to time and wind speed. Attached Figure Description

[0035] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments recorded in the embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0036] Figure 1 This is a flowchart of a method for long-term tracking and identification of key structural dynamic characteristics of wind turbines according to the present invention.

[0037] Figure 2 This is a flowchart of the synchronous information processing technology involved in this invention;

[0038] Figure 3 Set the order spectrum and bandpass filter width for the synchronization signal;

[0039] Figure 4 The peak extraction method is used to obtain the modal frequencies of each key component;

[0040] Figure 5 To extract the multi-mode damping corresponding to different key structures. Detailed Implementation

[0041] The present invention will be further described in conjunction with the accompanying drawings and embodiments. The described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art should fall within the protection scope of the present invention.

[0042] This invention extracts data from wind turbines under different operating conditions during long-term service, obtained by the wind turbine monitoring and control and data acquisition system. It preprocesses the vibration acceleration monitoring data and analyzes the time spectrum of each signal segment to determine the wind turbine's operating condition. When the determination is that the turbine is in operation, synchronous information processing technology is used to process the vibration acceleration signal synchronously, and a bandpass filter is used to remove interference information related to shaft operation. The spectrum of the signal after interference removal is then further analyzed, and the modal frequencies of each component in the signal are obtained using peak picking. When the wind turbine is determined to be in shutdown condition, bandpass filtering is applied to the frequency range where the energy concentration of the multi-modes requiring special attention is achieved. The response acceleration signals of the multi-modes requiring special attention, induced by random environmental excitation, are converted into free decay signals using a random decrement method. Damping information of different order modal components of the signal requiring special attention is obtained by fitting an exponential function model. Frequency and modal damping time history diagrams are plotted according to the time history of the multi-mode parameters of the key wind turbine structure, enabling long-term tracking and identification of the dynamic characteristics of the key structure of the wind turbine.

[0043] Specifically, see Figures 1 to 5 This invention provides an embodiment of a long-term tracking and identification method for the key structural dynamic characteristics of wind turbines, which includes the following steps:

[0044] Step S1: Obtain data on different operating conditions of the wind turbine during long-term service from the wind turbine monitoring and control and data acquisition system; simultaneously, group the acquired data by hour, including acceleration data Y. o and motor shaft speed data V o , where o represents the hourly number of the data.

[0045] Step S2: Determine the operating status of the fan corresponding to each set of data based on the motor shaft speed signal. If the determination result is an operating condition, proceed to step S3; if the determination result is a shutdown condition, proceed to step S4.

[0046] Step S3: Extract the multi-mode frequencies corresponding to different key structures, including the following steps:

[0047] S31: Based on the motor shaft speed data V o The signal, using synchronous information processing technology, is used to process the vibration acceleration signal Y. o The process involves processing the data to obtain the order spectrum of the synchronization order signal in the periodic domain. Synchronization information processing technology involves processing the motor shaft speed data V...o Instantaneous phase extraction is performed on the signal, and a synchronous acquisition clock with an integer period is generated using inverse function transformation; then, the synchronous acquisition clock is used to analyze the vibration acceleration signal Y. o After resampling, the synchronization order signal is obtained, and the order spectrum is obtained by performing a Fourier transform on the synchronization order signal.

[0048] S32: The order spectrum signal of the vibration acceleration signal is filtered by a bandpass filter to obtain multiple synchronous integer-order signals Y related to the rotating shaft. o (s), s=1,2,…,n; where n is the number of order responses related to the rotation axis that need to be removed. The filtering frequency band of the bandpass filter is [f star ,f cut ]; Filter starting frequency f star =0.95*n, the filter cutoff frequency is f cut =1.05*n.

[0049] S33: Remove the filtered signal from the original signal to obtain the synchronization signal YT, which has been freed from operational response interference. o =Y o -Y o (s).

[0050] S34: The signal after interference removal is resampled using the original sampling clock, and the synchronization signal is adjusted according to the timing sampling sequence of the original signal. O The signal is then processed in reverse to restore the original time-domain signal after removing runtime response interference.

[0051] S35: For the original signal Spectral analysis is performed, and information in the spectrum is extracted using the peak picking method. The modal frequencies of each key structure are obtained by reading the horizontal coordinates corresponding to the peak points.

[0052] Step S4: Extract the multi-mode damping corresponding to different key structures. When extracting the mode damping of a single structure, the following steps are included:

[0053] S41: First, for the original signal Y o Bandpass filtering is performed to obtain single-mode acceleration data for the structure of interest.

[0054] S42: Acceleration data processed using the random decrement method Converted into a free decay signal The acceleration signal of a single set of freely decaying signals can be expressed as:

[0055] S43: Perform a Hbert transform on a single set of freely decaying signals, i.e., construct the analytical equation H(t) = U(t)e iθ(t) Where U(t) represents the amplitude, it can be further expressed as

[0056] S44: Take the logarithm of the amplitude U(t) and obtain the modal damping by fitting.

[0057] Step S5: Repeat steps S2 and S4 to obtain the multi-order frequencies and modal damping of the wind turbine generator at each time number corresponding to the grouped data. Plot the calculated results of each key structure into frequency and modal damping time history diagrams according to the time history, achieving long-term tracking and identification of the dynamic characteristics of the key structures of the wind turbine generator. This allows for further research into the evolution of modal frequencies and damping in the key structures of the wind turbine generator over time, and the summarization of the health status and dynamic characteristics evolution of the key structures of the wind turbine generator from long-term tracking.

[0058] This invention enables periodic online modal parameter identification and analysis of key structures in wind turbines, thereby achieving long-term tracking and monitoring of the dynamic characteristics of these key structures.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for long-term tracking and identification of key structural dynamic characteristics of wind turbines, characterized in that, include: Step 1: Obtain acceleration vibration data and generator shaft speed data during long-term operation of the wind turbine, and group the obtained acceleration vibration data and generator shaft speed data. Step 2: Determine the operating status of the wind turbine corresponding to each set of data based on the generator shaft speed signal; Step 3: Based on the time history, independently draw the relevant frequency and modal damping time history diagrams for different key components of the wind turbine to achieve long-term tracking and identification of the key structural dynamic characteristics of the wind turbine. Step 2 includes: Step 21: When the wind turbine is in operation, the vibration acceleration signal is synchronously processed using synchronous information processing technology; interference information related to shaft operation is removed from the data obtained after synchronous processing using a bandpass filter; the original sampling clock is used to resample the data after interference removal to restore the signal; and the peak picking method is used to obtain the modal frequency information of each component in the signal spectrum of each data group.

2. The method according to claim 1, characterized in that, Step 21 includes: Step 211: Based on the motor shaft speed data, process the vibration acceleration signal using the synchronization information processing method to obtain the order spectrum of the synchronization order signal in the periodic domain. Step 212: For the order spectrum signal of the vibration acceleration signal, filter the synchronization order signal in the periodic domain and remove the order responses related to the rotating shaft to obtain the synchronization signal with the interference of the running response removed. Step 213: Using resampling in synchronous information processing technology, the acceleration vibration is reverse-processed according to the timing sampling sequence of the original vibration acceleration signal to restore the time domain signal of the original vibration acceleration signal after removing the interference of the running response. Step 214: Perform spectrum analysis on the time-domain signal, extract information from the spectrum using the peak picking method, and read the coordinates corresponding to the peak points to obtain the modal frequencies of each key structure.

3. The method according to claim 2, characterized in that, Step 211 includes: Instantaneous phase extraction is performed on the motor shaft speed data. A synchronous acquisition clock with an integer period is generated by inverse function transformation. The vibration acceleration signal is then resampled using the synchronous acquisition clock to obtain the synchronous order signal. The order spectrum is obtained by performing Fourier transform on the synchronous order signal.

4. The method according to claim 2, characterized in that, Step 212 includes: Step 2121: For the order spectrum signal of the vibration acceleration signal, use a bandpass filter to filter the synchronous order signal in the periodic domain to obtain multiple synchronous integer order signals related to the rotating shaft; remove the order response related to the rotating shaft from the order spectrum signal, and perform bandpass filtering on the order spectrum signal after removing the order response. Step 2122: Remove the bandpass filtered signal from the original vibration acceleration signal to obtain the synchronization signal with the interference removed from the running response.

5. The method according to claim 1, characterized in that, Step 2 includes: Step 22: When the wind turbine is in a shutdown state, bandpass filtering is performed on the frequency range of energy concentration of the multi-mode. The random decrement method is used to convert the response acceleration signal induced by random environmental excitation of the multi-mode into a free decay signal. The damping information of different order mode components of the wind turbine structure in each set of data is obtained by fitting the exponential function model.

6. The method according to claim 5, characterized in that, Step 22 includes: Step 221: Bandpass filter the vibration acceleration signal to obtain single-mode acceleration data of the key structure of the wind turbine. Step 222: Convert the single-mode acceleration data into a free decay signal using the random decrement method; Step 223: Perform a Hilbert transformation on the free decay signal, take the logarithm of the transformed amplitude, and obtain the modal damping by fitting.

7. The method according to claim 1, characterized in that, After step 2 and before step 3, the following is also included: Repeat step 2 to obtain the multi-order frequencies and modal damping of the wind turbine generator under each time number corresponding to the grouped data.

8. The method according to claim 1, characterized in that, Step 1 includes: Acceleration vibration data and generator shaft speed data of wind turbines during long-term service are obtained from the wind turbine monitoring and control and data acquisition system; at the same time, the obtained acceleration vibration data and generator shaft speed data are grouped and processed according to time.

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