Method, device, equipment and storage medium for monitoring defects of wind turbine blade

Abstract

This application discloses a method, apparatus, device, and storage medium for monitoring defects in wind turbine blades, relating to the field of acoustic monitoring technology. The method acquires the blade length, angular velocity, and signal transmission frequency of the wind turbine, and receives multiple acoustic excitation signals transmitted by the signal transmitters through multiple signal receiving devices. The multiple acoustic excitation signals received by each signal receiving device are filtered according to the blade length, angular velocity, and signal transmission frequency to obtain multiple fault characteristic signals corresponding to each signal receiving device. The instantaneous phase information of each signal receiving device is determined based on the multiple fault characteristic signals. The target instantaneous phase drift rate of the wind turbine blade is determined based on the multiple instantaneous phase information, and the defect monitoring result of the wind turbine is determined based on the target instantaneous phase drift rate and a preset detection threshold. The embodiments of this application improve the accuracy of blade defect monitoring.

Description

Technical Field

[0001] This application belongs to the field of acoustic monitoring technology, and in particular relates to a method, device, equipment and storage medium for monitoring defects in wind turbine blades. Background Technology

[0002] With the increasing global demand for green energy, the wind power industry has developed rapidly, and the operational reliability of wind turbines has gradually become a core concern in the operation and maintenance field. As the energy conversion component of the turbine, wind turbine blades are large in size and operate in complex environments. During long-term service, they inevitably endure multiple influences, including cyclic aerodynamic loads, extreme weather conditions, biological corrosion, and material aging. These factors can induce defects such as crack propagation, bond delamination, and structural fatigue damage inside or on the surface of the blades, leading to aerodynamic performance degradation and even serious structural failure accidents. Therefore, continuous and effective monitoring of the blade's operating status is a crucial measure to ensure the safe and stable operation of wind farms.

[0003] Existing methods often rely on technologies such as visual inspection under shutdown conditions, UAV image acquisition, and infrared thermal imaging, or online monitoring technologies such as vibration analysis and acoustic emission monitoring for detection. However, detection methods dependent on shutdown conditions can only obtain static surface information of the blades, with limited ability to identify internal damage and early cracks. While online detection technologies can be used to identify structural changes during operation, the signal path is easily interfered with by the complex structure of the turbine, making it difficult to accurately extract weak responses related to blade defects from background noise. Furthermore, these methods are susceptible to external factors such as sunlight and temperature, making it difficult to guarantee detection stability. Therefore, the accuracy of existing wind turbine blade defect monitoring is relatively low. Summary of the Invention

[0004] This application provides a method, apparatus, device, and storage medium for monitoring defects in wind turbine blades, in order to solve the problem of low accuracy in existing technologies for monitoring defects in wind turbine blades.

[0005] In a first aspect, embodiments of this application provide a method for monitoring defects in wind turbine blades. The wind turbine blades are equipped with signal transmitting devices, and the wind turbine tower is equipped with multiple signal receiving devices. The method includes: The system acquires the operating information of the wind turbine and receives multiple acoustic excitation signals sent by the signal transmitter through multiple signal receiving devices. The operating information includes blade length, blade rotation angular velocity, and signal transmission frequency of the signal transmitter. Based on the blade length, angular velocity, and signal transmission frequency, multiple acoustic excitation signals received by each signal receiving device are filtered to obtain multiple fault characteristic signals corresponding to each signal receiving device. The instantaneous phase information of each signal receiving device is determined based on multiple fault characteristic signals corresponding to each signal receiving device; The target instantaneous phase drift rate of the wind turbine blades is determined based on multiple instantaneous phase information, and the defect monitoring results of the wind turbine are determined based on the target instantaneous phase drift rate and the preset detection threshold.

[0006] Secondly, embodiments of this application provide a device for monitoring defects in wind turbine blades. The wind turbine blades are equipped with signal transmitting devices, and the wind turbine tower is equipped with multiple signal receiving devices. The device includes: The acquisition module is used to acquire the operating information of the wind turbine and receive multiple acoustic excitation signals sent by the signal transmitter through multiple signal receiving devices. The operating information includes blade length, blade rotation angular velocity and signal transmission frequency of the signal transmitter. The filtering module is used to filter multiple acoustic excitation signals received by each signal receiving device according to the blade length, angular velocity and signal transmission frequency, so as to obtain multiple fault characteristic signals corresponding to each signal receiving device. The determination module is used to determine the instantaneous phase information of each signal receiving device based on multiple fault characteristic signals corresponding to each signal receiving device; The determination module is also used to determine the target instantaneous phase drift rate of the wind turbine blades based on multiple instantaneous phase information, and to determine the defect monitoring results of the wind turbine based on the target instantaneous phase drift rate and a preset detection threshold.

[0007] Thirdly, embodiments of this application provide a terminal device, which includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the method for monitoring wind turbine blade defects as described in the first aspect.

[0008] Fourthly, embodiments of this application provide a computer-readable storage medium storing computer program instructions, which, when executed by a processor, implement the method for monitoring wind turbine blade defects as described in the first aspect.

[0009] Fifthly, embodiments of this application provide a computer program product in which instructions, when executed by a processor of an electronic device, cause the electronic device to perform a method for monitoring wind turbine blade defects as described in the first aspect.

[0010] This application provides a method, apparatus, device, and storage medium for monitoring defects in wind turbine blades. The wind turbine blades are equipped with signal transmitting devices, and the tower of the wind turbine is equipped with multiple signal receiving devices. First, the operating information of the wind turbine is acquired, and multiple acoustic excitation signals transmitted by the signal transmitting devices are obtained through the multiple signal receiving devices. The operating information includes blade length, blade rotation angular velocity, and signal transmission frequency of the signal transmitting devices. This provides accurate data for subsequent filtering. Multiple signal receiving devices cover the entire blade length, avoiding blind spots of single sensors and ensuring no fault signals are missed. The multiple acoustic excitation signals are filtered based on blade length, angular velocity, and signal transmission frequency to obtain multiple fault characteristic signals. Irrelevant environmental noise is filtered out, retaining only the defect characteristic signals caused by blade defects, preventing interference from irrelevant signals. The instantaneous phase information of each signal receiving device is determined based on the multiple fault characteristic signals. The instantaneous phase information extracted from multiple signal receiving devices provides independent and accurate basic data for subsequently determining the fused instantaneous phase drift rate of the wind turbine blades, avoiding random errors from single-phase data and improving the reliability of defect judgment. The target instantaneous phase drift rate of the wind turbine blade is determined based on multiple instantaneous phase information. The defect monitoring result of the wind turbine is then determined based on the target instantaneous phase drift rate and a preset detection threshold. The instantaneous phase drift rate is calculated based on continuous phase information and directly correlated with blade defects. Multi-channel phase drift rate fusion processing reduces single-channel noise interference and improves the identification of abnormal signals. Therefore, the embodiments of this application improve the accuracy of wind turbine blade defect monitoring. Attached Figure Description

[0011] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments of this application will be briefly introduced below. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0012] Figure 1 This is a flowchart illustrating the method for monitoring defects in wind turbine blades provided in this application embodiment; Figure 2 This is a flowchart illustrating the method for determining fault characteristic signals provided in an embodiment of this application; Figure 3 This is a flowchart illustrating the method for locating defects in wind turbine blades provided in an embodiment of this application. Figure 4 This is a schematic diagram of the wind turbine experimental unit provided in the embodiments of this application; Figure 5 This is a schematic diagram of the multi-channel fusion anomaly evaluation results provided in an embodiment of this application; Figure 6 This is a schematic diagram of the wind turbine blade damage location results provided in the embodiments of this application; Figure 7 This is a schematic diagram of the structure of the device for monitoring wind turbine blade defects provided in the embodiments of this application; Figure 8 This is a schematic diagram of the structure of the terminal device provided in the embodiments of this application. Detailed Implementation

[0013] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are intended only to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0014] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0015] With the increasing global demand for green energy, the wind power industry has developed rapidly, and the operational reliability of wind turbines has gradually become a core concern in the operation and maintenance field. As the energy conversion component of the turbine, wind turbine blades are large in size and operate in complex environments. During long-term service, they inevitably endure multiple influences, including cyclic aerodynamic loads, extreme weather conditions, biological corrosion, and material aging. These factors can induce defects such as crack propagation, bond delamination, and structural fatigue damage inside or on the surface of the blades, leading to aerodynamic performance degradation and even serious structural failure accidents. Therefore, continuous and effective monitoring of the blade's operating status is a crucial measure to ensure the safe and stable operation of wind farms.

[0016] While methods for monitoring blade defects are constantly evolving, shortcomings remain. Current engineering techniques such as visual inspection, UAV image acquisition, and infrared thermal imaging rely on shutdown conditions and can only acquire static surface information of the blades, offering limited ability to identify internal damage and early cracks. Furthermore, these methods are susceptible to external factors such as light and temperature, making it difficult to guarantee detection stability. Online monitoring technologies, such as vibration analysis and acoustic emission monitoring, can be used to identify structural changes during operation, but the signal paths are easily interfered with by the complex structure of the generator unit, making it difficult to accurately extract weak responses related to blade defects from background noise. Additionally, their multi-channel information fusion and localization capabilities are relatively insufficient.

[0017] To address the problems of existing technologies, this application provides a method, apparatus, device, and storage medium for monitoring defects in wind turbine blades. The wind turbine blades are equipped with signal transmitting devices, and the tower of the wind turbine is equipped with multiple signal receiving devices. First, the operating information of the wind turbine is acquired, and multiple acoustic excitation signals transmitted by the signal transmitting devices are obtained through the multiple signal receiving devices. The operating information includes blade length, blade rotation angular velocity, and signal transmission frequency of the signal transmitting devices. This provides accurate data for subsequent filtering. Multiple signal receiving devices cover the entire blade length, avoiding blind spots of single sensors and ensuring no fault signals are missed. The multiple acoustic excitation signals are filtered based on blade length, angular velocity, and signal transmission frequency to obtain multiple fault characteristic signals. Irrelevant environmental noise is filtered out, retaining only the defect characteristic signals caused by blade defects, preventing interference from irrelevant signals. The instantaneous phase information of each signal receiving device is determined based on the multiple fault characteristic signals. The instantaneous phase information extracted from multiple signal receiving devices provides independent and accurate basic data for subsequently determining the fused instantaneous phase drift rate of the wind turbine blades, avoiding random errors from single-phase data and improving the reliability of defect judgment. The target instantaneous phase drift rate of the wind turbine blade is determined based on multiple instantaneous phase information. The defect monitoring result of the wind turbine is then determined based on the target instantaneous phase drift rate and a preset detection threshold. The instantaneous phase drift rate is calculated based on continuous phase information and directly correlated with blade defects. Multi-channel phase drift rate fusion processing reduces single-channel noise interference and improves the identification of abnormal signals. Therefore, the embodiments of this application improve the accuracy of wind turbine blade defect monitoring.

[0018] The following section first introduces a method for monitoring defects in wind turbine blades provided in the embodiments of this application.

[0019] Figure 1 This diagram illustrates a flowchart of a method for detecting defects in wind turbine blades according to an embodiment of this application. The wind turbine blades are equipped with signal transmitting devices, and the wind turbine tower is equipped with multiple signal receiving devices, such as... Figure 1 As shown, the method may include the following steps: S101 to S104.

[0020] S101, acquire the operating information of the wind turbine, and receive multiple acoustic excitation signals sent by the signal transmitter through multiple signal receiving devices. The operating information includes blade length, blade rotation angular velocity and signal transmission frequency of the signal transmitter.

[0021] The wind turbine's operational information comprises parameters related to the blade's operating status and signal transmission characteristics. The signal transmitting device, mounted on the blade, is used to actively transmit specific forms of acoustic signals, providing a stable acoustic excitation source. The signal receiving device, mounted on the tower, is used to capture the acoustic excitation signal transmitted through the blade. The acoustic excitation signal is the acoustic signal transmitted by the signal transmitting device, propagated through the blade, and received; it contains information about the blade's structural state.

[0022] In some embodiments, the blade length can be acquired and fixed during installation, without the need for real-time acquisition; the blade rotational angular velocity can be monitored in real time by a speed sensor at the wind turbine hub; and the signal transmission frequency is a preset fixed value for the transmitting device.

[0023] In some embodiments, the signal transmitting device continuously transmits an active acoustic excitation signal, which penetrates the blade structure and is synchronously captured by multiple signal receiving devices at different heights on the inner wall of the tower.

[0024] This application embodiment simultaneously acquires equipment operating parameters and acoustic excitation signals. The operating parameters provide a precise basis for subsequent signal filtering, and the multi-channel acoustic excitation signal avoids the blind spots of single-channel acquisition, ensuring that defect signals at different locations on the blade can be captured. Both operating information and acoustic excitation signals are acquired in real time without shutdown, meeting the needs of online monitoring of wind turbines and solving the problem of low efficiency in traditional shutdown inspections. The signal transmitting device actively transmits signals. Compared with passively receiving environmental signals, the acoustic excitation signal strength is controllable and the frequency is fixed, making it easier to distinguish between valid and interference signals during subsequent processing. Multi-channel acquisition reduces the risk of data failure due to the failure of a single sensor.

[0025] In some embodiments, when the signal transmitting device transmits an FM acoustic excitation signal, it can send signals of different frequencies instead of fixed frequencies for the detection of different types of defects.

[0026] This application embodiment uses signals of different frequencies to detect different types of defects. When the excitation frequency is close to or equal to the defect's natural frequency, the defect will resonate, and the vibration amplitude will be significantly amplified, improving the adaptability to different types of defects and thus improving the accuracy of defect monitoring.

[0027] S102, based on the blade length, angular velocity and signal transmission frequency, filters the multiple acoustic excitation signals received by each signal receiving device to obtain multiple fault characteristic signals corresponding to each signal receiving device.

[0028] Filtering refers to the signal processing procedure that uses specific algorithms or equipment to select target frequency components and remove interference components based on the signal's frequency characteristics. The fault characteristic signal is the signal component that, after filtering, contains only information related to blade defects.

[0029] In some embodiments, the blade length determines the effective path range of signal propagation, the angular velocity reflects the dynamic influence of blade rotation on the signal, and the signal transmission frequency is the reference frequency of the effective signal. By establishing a correlation model between these three factors and the acoustic excitation signal, the frequency and amplitude variation patterns of the signal related to blade defects are clarified. Based on the above correlation model, signal components in the original acoustic excitation signal that exceed the effective frequency range or exhibit irregular amplitude fluctuations are removed. Signals that conform to the characteristics of blade defect signals after interference removal are retained, forming the fault characteristic signal corresponding to each signal receiving device.

[0030] The filtering in this embodiment is based on directly correlated core operating parameters of the wind turbine, rather than fixed filtering rules. This allows for dynamic adaptation to different blade operating states, ensuring that fault characteristic signals are not mistakenly filtered and interference signals are effectively eliminated. It is adaptable to wind turbines of different specifications; only the correlation model needs to be adjusted according to the specific unit's operating parameters, eliminating the need to redesign the filtering algorithm and reducing application costs.

[0031] In some embodiments, a correlation model between operating parameters and signal characteristics can be constructed based on machine learning algorithms. The model is trained by a large amount of signal data under normal and fault conditions, enabling the model to automatically identify the frequency and amplitude patterns of fault characteristic signals and achieve intelligent removal of interference components. This is suitable for wind fields with complex wind conditions and diverse interference.

[0032] In some embodiments, the filtering sensitivity can be dynamically adjusted, and the threshold for interference removal can be adjusted according to real-time wind speed changes. For example, the threshold can be appropriately increased when the wind speed is high to reduce wind noise interference, and the threshold can be decreased when the wind speed is low to avoid missing weak fault signals and improve the filtering effect under complex wind conditions.

[0033] S103, determine the instantaneous phase information of each signal receiving device based on the multiple fault characteristic signals corresponding to each signal receiving device.

[0034] Among them, instantaneous phase information is the phase state of the fault characteristic signal at any time, which directly reflects the time-domain fluctuation characteristics of the signal. When there is a defect in the blade, the instantaneous phase will show regular anomalies.

[0035] In some embodiments, based on the fault characteristic signal, the instantaneous phase is calculated using the periodicity and amplitude variation law of the signal, and the phase value at each moment is arranged in chronological order to form a continuous sequence of instantaneous phase information, ensuring that each signal receiving device corresponds to a complete set of phase data and obtains instantaneous phase information.

[0036] This application embodiment obtains instantaneous phase information for subsequent defect judgment. The instantaneous phase is directly affected by the signal propagation path and structural state. Small defects in the blade will cause subtle changes in the signal phase. Compared with amplitude signals, phase information can capture early defects earlier and more sensitively, improving the accuracy of subsequent defect monitoring.

[0037] In some embodiments, the phase can be extracted based on Fourier transform, and Fourier decomposition can be performed on the fault feature signal to obtain the frequency domain components of the signal. The phase values ​​corresponding to each frequency component can be directly read from the frequency domain spectrum to form instantaneous phase information, which is suitable for fault feature signals with a single frequency component.

[0038] In some embodiments, the phase can be extracted based on the signal envelope, the envelope of the fault characteristic signal can be obtained through Hilbert transform, and the instantaneous phase can be calculated based on the amplitude change rate of the envelope. This method is suitable for fault characteristic signals with large amplitude fluctuations.

[0039] In some embodiments, cross-correlation analysis can be used to extract the phase. The original excitation signal of the signal transmitting device is used as a reference to calculate the cross-correlation coefficient between the fault characteristic signal and the reference signal. The instantaneous phase is determined based on the peak position of the correlation coefficient, which improves the accuracy of phase extraction and is suitable for scenarios with less interference residue.

[0040] S104, determine the target instantaneous phase drift rate of the wind turbine blade based on multiple instantaneous phase information, and determine the defect monitoring result of the wind turbine based on the target instantaneous phase drift rate and the preset detection threshold.

[0041] The instantaneous phase drift rate is the rate of change of the instantaneous phase over time, reflecting the speed of phase change. When there are defects in the blade, the phase drift rate will exhibit abnormal fluctuations. The target instantaneous phase drift rate is a comprehensive index obtained by fusing the instantaneous phase drift rates from multiple signal receiving devices, used to uniformly determine the blade status. The preset detection threshold is a judgment critical value set based on the statistical results of the phase drift rate of normal blades, used to distinguish between normal and abnormal blade states. Multi-channel fusion is the process of comprehensively processing the detection data from multiple signal receiving devices to obtain a unified result.

[0042] In some embodiments, the instantaneous phase drift rate of a single channel is first calculated based on the continuous instantaneous phase information of each signal receiving device. A preset fusion method (such as weighted average or median fusion) is used to process the instantaneous phase drift rates of all receiving devices to obtain a target instantaneous phase drift rate. Finally, the target instantaneous phase drift rate is compared with a preset detection threshold. If the target instantaneous phase drift rate is greater than the preset detection threshold, it indicates an abnormal phase drift rate, and the blade is determined to have a defect; if the target instantaneous phase drift rate does not exceed the preset detection threshold, it indicates that the phase drift rate is within the normal range, and the blade is determined to be in normal condition.

[0043] In some embodiments, a preset detection threshold can be dynamically set based on parameters such as blade running time, ambient temperature, and wind speed to improve the adaptability of the judgment and avoid misjudgment under different operating conditions with a fixed threshold.

[0044] This application embodiment acquires comprehensive and accurate wind turbine operating information and multi-channel acoustic excitation signals in real time, providing precise parameter basis for subsequent signal processing. Multi-channel acquisition avoids blind spots in single-channel monitoring, ensuring that defect signals from different areas of the blade are captured. By filtering based on operating parameters, fault feature signals are extracted, and instantaneous phase information is extracted from these signals. Compared to amplitude signals, this method is more sensitive in capturing structural changes caused by early-stage minor defects in the blade, solving the problem of low sensitivity in traditional signal feature extraction. Finally, the target drift rate is obtained by fusing the instantaneous phase drift rates of multiple channels. Combined with a preset threshold, defect judgment is achieved. This reduces the random interference of single-channel data and amplifies defect features through the drift rate, improving the reliability and accuracy of the judgment. Furthermore, the entire process requires no downtime, achieving online real-time monitoring and avoiding the drawbacks of traditional downtime inspections, such as low efficiency and inability to detect defects in a timely manner.

[0045] In some embodiments, the active acoustic excitation signal s(t) emitted by the signal transmitting device can be:

[0046] in, The amplitude of the active acoustic excitation signal. The frequency of the active acoustic excitation signal, Let t be the initial phase and t be the time.

[0047] In some embodiments, the signal transmitting device is located at the center of the blade root cover plate, and the transmitting surface of the signal transmitting device faces the blade cavity; multiple signal receiving devices are installed at intervals from top to bottom along the inner wall of the wind turbine tower, and the signal receiving devices are located on the main windward side of the tower.

[0048] In some embodiments, the signal transmitting device can be installed in the geometric center region of the blade root cover by bolting or bonding. The emitting surface of the signal transmitting device faces the inside of the blade cavity, ensuring that the sound waves propagate mainly along the blade cavity direction and cover the core area of ​​the blade's internal structure.

[0049] This embodiment of the application, by installing the signal transmitting device in the center of the blade root cover plate with the transmitting surface facing the blade cavity, ensures that the acoustic excitation signal is uniformly radiated along the blade length, with low propagation loss and stable structural installation. It also enables the signal to be focused on the core area inside the blade, providing a strong and stable signal source for defect monitoring. At the same time, by installing multiple signal receiving devices at intervals from top to bottom along the inner wall of the main windward side of the tower, it achieves signal coverage along the entire blade length, avoiding monitoring blind spots, and utilizes the shortest signal propagation path on the main windward side to reduce sound wave attenuation and environmental interference, thereby improving the reception efficiency of the effective signal.

[0050] In some embodiments, the signal receiving devices are installed from top to bottom on the inner wall of the wind turbine tower, and need to be installed on the inner wall of the tower on the main windward side of the wind turbine. The distance between all signal receiving devices needs to cover the entire blade length, assuming the blade length is... The installation spacing of the signal receiving device is Therefore, the number N of signal receiving devices that need to be installed is:

[0051] The active acoustic excitation signal transmitted from the blades and received by the signal receiving device is expressed in the following form:

[0052] in, Let be the active acoustic excitation signal received by the i-th signal receiving device at time t.

[0053] In some embodiments, the signal receiving device is installed with non-uniform spacing, that is, the spacing is adjusted according to the defect risk level and structural characteristics of different areas of the blade. For example, the sensor spacing is shortened in the middle section of the blade where damage is likely to occur, which ensures that there are no blind spots in the monitoring along the entire length of the blade, and also specifically increases the signal acquisition density in areas with high defect incidence.

[0054] In some embodiments, such as Figure 2 As shown, multiple acoustic excitation signals corresponding to each signal receiving device are filtered according to the blade length, angular velocity and signal transmission frequency to obtain multiple fault characteristic signals corresponding to each signal receiving device, which may include: S201 to S204.

[0055] S201, determine the upper limit and lower limit of the frequency change of the acoustic excitation signal based on the blade length, angular velocity and signal transmission frequency, and determine the frequency change range based on the upper limit and lower limit of the frequency change.

[0056] The upper limit of frequency variation is the maximum effective frequency value that the acoustic excitation signal may exhibit, representing the upper frequency boundary of the effective signal. The lower limit of frequency variation is the minimum effective frequency value that the acoustic excitation signal may exhibit, representing the lower frequency boundary of the effective signal. The frequency variation range is a continuous frequency range consisting of the upper and lower limits, encompassing all effective frequency components of the acoustic excitation signal during blade rotation.

[0057] In some embodiments, based on the rotational speed information of the wind turbine blades, the frequency change of the active acoustic excitation signal caused by the Doppler effect when the blades rotate closer to the tower is as follows:

[0058] In the formula, This represents the upper limit of frequency variation. Represents the frequency of the active acoustic excitation signal. Let be the angular velocity of the blade. The distance from the leaf root to the leaf blade is represented by the following: The location, It represents the speed at which sound travels.

[0059] As the blades rotate away from the tower, the frequency change of the active acoustic excitation signal caused by the Doppler effect is as follows:

[0060] in, This represents the lower limit of frequency variation.

[0061] The embodiments of this application are based on length, angular velocity and signal transmission frequency calculations, rather than fixed intervals, and can be dynamically adjusted according to the blade operating status to adapt to blades with different rotational speeds and lengths, thereby improving the adaptability of filtering.

[0062] S202, bandpass filtering is performed on multiple acoustic excitation signals corresponding to each signal receiving device according to the frequency change range to obtain multiple first filtered signals.

[0063] Bandpass filtering is a filtering method that allows signals within a frequency variation range to pass through, while attenuating or blocking signals outside the range. The first filtered signal is the intermediate signal obtained after bandpass filtering, which has removed interference from outside the range, but still contains the original fixed-frequency component of the acoustic excitation signal.

[0064] In some embodiments, the original acoustic excitation signal acquired by each signal receiving device is input into a filter. The filter only allows signals with frequencies within the frequency variation range and significantly attenuates signals outside the range. Each signal receiving device corresponds to a first filtered signal, which has removed most of the external interference but still contains the original fixed-frequency component of the active acoustic excitation signal.

[0065] In some embodiments, in order to eliminate the interference of other frequency components, the active acoustic excitation signal acquired by the signal receiving device is bandpass filtered:

[0066] in, Let be the signal obtained by the i-th signal receiving device after bandpass filtering. This is a bandpass filter function.

[0067] This application's embodiments accurately filter out environmental noise and mechanical clutter outside the frequency variation range, significantly reducing interference from irrelevant signals to subsequent processing and improving the signal-to-noise ratio. Filtering is performed only on a single frequency range, resulting in low algorithm complexity and the ability to process multi-channel signals in real time, meeting the real-time requirements of online monitoring of wind turbines.

[0068] S203, determine the upper limit and lower limit of frequency suppression according to the signal transmission frequency and the preset half-bandwidth parameter, and determine the frequency suppression range of the acoustic excitation signal according to the upper limit and lower limit of frequency suppression.

[0069] The preset half-bandwidth parameter is a manually set parameter used to determine the width of the frequency suppression interval, thus determining the suppression range of the band-stop filter. It can be dynamically adjusted according to the stability of the signal transmission frequency and the equipment operating conditions to adapt to different types of signal transmitting devices and operating environments. The upper limit of frequency suppression is the maximum frequency value of the frequency suppression interval. The lower limit of frequency suppression is the minimum frequency value of the frequency suppression interval. The frequency suppression interval is the frequency range formed by the upper and lower limits, used to remove the original fixed-frequency components of the acoustic excitation signal.

[0070] Based on the center frequency of the frequency variation range and the preset half bandwidth, the embodiments of this application accurately lock the fixed frequency component range of the original acoustic excitation, avoiding the loss of defective signals due to an excessively large suppression range, or the residual fixed frequency interference due to an excessively small suppression range.

[0071] S204, band-stop filtering is performed on multiple first filtered signals corresponding to each signal receiving device according to the frequency suppression interval to obtain multiple fault characteristic signals.

[0072] Band-stop filtering is a filtering method that blocks signals within the frequency suppression range while allowing signals outside the range to transmit normally, and is used to remove fixed-frequency interference. The fault characteristic signal is the final effective signal obtained after two stages of band-pass and band-stop filtering, which only contains characteristics such as frequency shift and amplitude fluctuation caused by blade defects.

[0073] In some embodiments, after bandpass filtering of the active acoustic excitation signal, it is necessary to further filter out the fixed-frequency components contained therein, that is:

[0074] in, The fault characteristic signal is obtained by the i-th signal receiving device after band-stop filtering. It is a band-stop filter function. This represents the half-bandwidth parameter of the band-stop filter. This is the lower limit of frequency suppression. This is the upper limit of frequency suppression.

[0075] In some embodiments, a band-stop filter with high stopband attenuation and low passband distortion is selected to ensure that the fixed-frequency signal within the suppression interval is deeply attenuated. The first filtered signal corresponding to each signal receiving device is input to the band-stop filter. The filter attenuates the fixed-frequency components within the frequency suppression interval, allowing signals carrying defect characteristics outside the interval to pass normally. Each signal receiving device outputs a set of fault characteristic signals, which have been freed from environmental noise and fixed-frequency interference, retaining only the signal changes caused by blade defects.

[0076] This embodiment first accurately calculates the upper and lower limits and intervals of frequency variation based on blade length, angular velocity, and signal transmission frequency. Then, it efficiently removes environmental noise and mechanical clutter outside the interval using bandpass filtering, obtaining a high signal-to-noise ratio first filtered signal. Next, based on the center frequency of the frequency variation interval and a preset half-bandwidth parameter, it precisely identifies the range of fixed-frequency components to be removed, forming a frequency suppression interval. Through deep attenuation of fixed-frequency interference using bandstop filtering, it retains characteristic signals such as frequency shifts caused by blade defects, ultimately obtaining a high-purity fault characteristic signal. This approach solves the problems of weak anti-interference capability and poor adaptability of traditional filtering methods, while ensuring the fidelity and recognizability of the defect characteristic signal, significantly improving the accuracy and reliability of subsequent wind turbine blade defect monitoring.

[0077] In some embodiments, determining the instantaneous phase information of each signal receiving device based on multiple fault characteristic signals corresponding to each signal receiving device may include: For each signal receiving device, a phase shift operation is performed on multiple fault characteristic signals to obtain the corresponding imaginary part signal, and the instantaneous phase value of the corresponding fault characteristic signal is determined based on the fault characteristic signal and the imaginary part signal. Among them, the phase shift operation is the process of performing a specific transformation on the fault characteristic signal to obtain an auxiliary signal with a fixed phase shift from the original signal; the imaginary part signal is the signal component obtained after the phase shift operation; and the instantaneous phase value is the phase state of the fault characteristic signal at a certain moment.

[0078] When the difference between adjacent instantaneous phase values ​​of a fault characteristic signal exceeds a set threshold, the next instantaneous phase value is corrected according to a preset correction value to obtain continuous instantaneous phase information for each signal receiving device. The difference between adjacent instantaneous phase values ​​is the difference between the instantaneous phase values ​​of two consecutive sampling times, reflecting the rate of phase change. The set threshold is a preset critical value based on the phase change law, used to determine whether phase entanglement exists. The preset correction value is a fixed value used to correct phase entanglement, ensuring that the phase sequence changes continuously after correction. Continuous instantaneous phase information is a phase sequence in which the phase value shows a steady increasing or decreasing trend along the time axis after correction, without jumps or entanglement.

[0079] This application first performs a phase shift operation on the fault feature signal to obtain an orthogonal imaginary part signal. Based on a complex model, the instantaneous phase value is accurately calculated, avoiding amplitude interference from single signal phase extraction and ensuring the accuracy of the basic phase data. Then, by judging the relationship between the difference between adjacent instantaneous phase values ​​and a set threshold, a preset correction value is used to correct the entangled phase, effectively eliminating phase jumps and obtaining continuous and stable instantaneous phase information. This solves the information distortion problem caused by entanglement in traditional phase extraction. It can adapt to fault feature signals of different frequencies and qualities, and provides high-fidelity phase data for subsequent target instantaneous phase drift rate calculation and defect location model establishment, significantly improving the reliability and accuracy of subsequent wind turbine blade defect monitoring.

[0080] In some embodiments, performing a phase shift operation on multiple fault characteristic signals corresponding to each signal receiving device to obtain the corresponding imaginary part signal, and determining the instantaneous phase value of the corresponding fault characteristic signal based on the fault characteristic signal and the imaginary part signal, may include: For the filtered acoustic excitation signal The corresponding analytic signal is constructed using the Hilbert transform:

[0081] in, Let be the analyzed signal of the fault characteristic signal of the i-th signal receiving device at time t. Indicates the signal The imaginary part of the signal obtained by performing the Hilbert transform. It represents the imaginary unit.

[0082] Using the constructed analytic signal Thus, the instantaneous phase information of the corresponding acoustic excitation signal can be obtained, that is:

[0083] in, Let be the instantaneous phase of the fault characteristic signal of the i-th signal receiving device at time t.

[0084] In some embodiments, the obtained instantaneous phase information is further unwound to convert the wound phase information into continuously changing phase values:

[0085] in, Let be the instantaneous phase of the fault characteristic signal of the i-th signal receiving device after unwinding at time t. This represents the unwinding function.

[0086] In some embodiments, determining the target instantaneous phase drift rate of the wind turbine blade based on multiple instantaneous phase information may include: The instantaneous phase drift rate is determined based on each instantaneous phase information; where the instantaneous phase drift rate is the rate of change of the instantaneous phase information collected by a single signal receiving device over time.

[0087] The instantaneous phase drift rates of all signal receiving devices are fused to obtain the target instantaneous phase drift rate of the wind turbine blades; the fusion process is a process of comprehensively calculating the instantaneous phase drift rate sequences of multiple channels using a preset algorithm.

[0088] In this embodiment, the instantaneous phase drift rate is calculated independently for the instantaneous phase information of each signal receiving device. This avoids cross-interference of multi-channel data, ensures that the monitoring characteristics of different areas of the blade are truly preserved by each channel, and amplifies the phase changes caused by minor defects in the blade through time difference, providing high-sensitivity and high-independence basic data for subsequent fusion. Then, through multi-channel drift rate fusion processing, the random noise and local interference of single channels are canceled out, and the common characteristics reflecting the overall state of the blade are extracted, making the target instantaneous phase drift rate more stable and reliable.

[0089] In some embodiments, the instantaneous phase drift rate is determined based on each instantaneous phase information, and the calculation formula is as follows:

[0090] in, Let be the instantaneous phase drift rate of the i-th signal receiving device at time t. Let be the instantaneous phase of the fault characteristic signal of the i-th signal receiving device after unwinding at time t. For the fault characteristic signal of the i-th signal receiving device in The instantaneous phase after unwinding at a given moment. It represents the time difference between two adjacent sampling times.

[0091] In some embodiments, the instantaneous phase drift rates of all signal receiving devices are fused to obtain the target instantaneous phase drift rate of the wind turbine blades. The calculation formula can be:

[0092] Where M(t) is the instantaneous phase drift rate of the target, and N is the total number of signal receiving devices. and Represents the weighting parameter.

[0093] In some embodiments, the defect monitoring results of the wind turbine are determined based on the target instantaneous phase drift rate and a preset detection threshold, and the formula can be:

[0094] in, For the set anomaly detection threshold, when When the wind turbine blades are intact, it indicates a defect in the blades; conversely, when they are not intact, it indicates a defect in the blades. When this occurs, it indicates that the blades are operating normally.

[0095] In some embodiments, such as Figure 3 As shown, after determining the defect monitoring results of the wind turbine based on the target instantaneous phase drift rate and the preset detection threshold, the method may further include: S301 and S302.

[0096] S301, acquire the theoretical phase information of the theoretical defect characteristic signal corresponding to different defect locations for each signal receiving device.

[0097] Among them, the theoretical phase information of the theoretical defect characteristic signal is the acoustic excitation signal phase data corresponding to a specific defect location, calculated based on the acoustic propagation model and blade structural parameters, and serves as a benchmark for comparison with the measured phase. The defect location is the potential defect distribution point covering the entire length of the blade.

[0098] In some embodiments, before acquiring the theoretical phase information of the theoretical defect feature signals at different defect locations corresponding to each signal receiving device, the method further includes: By leveraging the physical relationship between the phase change and path change of the acoustic excitation signal, a forward propagation model for radial localization of blade defects is established. Specific steps include: Based on the relationship between the propagation path change and phase change of the acoustic excitation signal, the phase measurement is converted into a geometric distance measurement, as follows:

[0099] in, and These represent phase change and distance change, respectively. The wavelength represents the acoustic excitation signal.

[0100] Assuming the defect is located at a radial distance of 1 / 2000 from the wind turbine blade. If the location is such that the forward propagation model for radial localization of blade defects is constructed as follows:

[0101] in, Let be the phase of the propagated signal of the i-th signal receiving device at the radial position r of the blade. For the first The height difference between the signal receiving devices and the leaf root This is the initial phase.

[0102] S302, determine the defect location based on the phase residual corresponding to each signal receiving device and the preset weight, so that the weighted sum of all phase residuals at the defect location is less than a set threshold; the phase residual is the difference between the instantaneous phase information and the theoretical phase information of the defect location.

[0103] Here, the phase residual is the difference between the measured instantaneous phase information of a single signal receiving device and the theoretical phase information corresponding to a certain defect location, reflecting the degree of deviation between the measured and theoretical values. The preset weight is a weighting coefficient assigned to each signal receiving device to reflect the reliability of the sensor data. The set threshold is the critical value for determining whether the defect location matches.

[0104] This application embodiment obtains the theoretical phase information of each signal receiving device corresponding to different defect locations, and establishes an accurate theoretical reference matrix based on blade structural parameters and acoustic propagation models. This achieves full coverage of defect locations along the entire blade length, and ensures the accuracy of the theoretical phase by adapting to different unit parameters and dynamically correcting environmental influences, providing a reliable comparison basis for defect location. Furthermore, by calculating the phase residual between the measured instantaneous phase and the theoretical phase, and combining it with preset weights to calculate a weighted sum and filter out defect locations less than a set threshold, this approach highlights the role of highly reliable sensors, suppresses the influence of low-quality signals and environmental interference, and effectively reduces positioning deviations caused by random errors.

[0105] In some embodiments, determining the defect location based on the phase residual corresponding to each signal receiving device and a preset weight, such that the weighted sum of all phase residuals at the defect location is less than a set threshold, may include: The initial positioning position is determined based on the installation height of the signal receiving device with the highest signal-to-noise ratio and the instantaneous phase information, and the initial phase residual of the initial positioning position is also determined. Among them, the signal receiving device with the highest signal-to-noise ratio is the one with the largest effective signal-to-noise ratio and the strongest data reliability among multiple sensors; the installation height is the vertical height of the signal receiving device installed along the inner wall of the tower; the initial positioning position is the defect location initially estimated based on high-reliability sensor data, which is the starting point for iterative optimization. The initial phase residual is the difference between the theoretical phase corresponding to the initial positioning position and the instantaneous phase measured by all sensors, reflecting the degree of deviation in the initial positioning; The first positioning position is determined based on the initial phase residual, preset weights, and initial positioning position, and the first phase residual of the first positioning position is determined accordingly. The first positioning position is the defect position obtained after the first correction of the initial positioning position based on the initial phase residual and preset weights, which is an intermediate result of iterative optimization; the first phase residual is the difference between the theoretical phase corresponding to the first positioning position and the instantaneous phase measured by all sensors, reflecting the degree of deviation after the first correction; the preset weights are fixed coefficients assigned to each sensor to reflect the reliability of the sensor data, for example, sensors with high signal-to-noise ratios have higher weights. The second positioning position is determined based on the first phase residual, the preset weight, and the first positioning position, and the second phase residual of the second positioning position is also determined. The second positioning position is the defect position obtained after correcting the first positioning position a second time based on the first phase residual and preset weights; the second phase residual is the difference between the theoretical phase corresponding to the second positioning position and the instantaneous phase measured by all sensors, reflecting the final deviation after the second correction. If the difference between the first and second positioning positions is less than a first set threshold, or if the weighted sum of the second phase residuals of the second positioning positions is less than a second set threshold, the second positioning position is determined as the defect positioning position. The first set threshold is a critical value for judging the convergence of the two positioning positions, reflecting the stability of the positioning position. The second set threshold is a critical value for judging whether the weighted sum of the second phase residuals is the minimum, reflecting the degree of matching between the positioning position and the measured data.

[0106] This application uses data from the signal receiving device with the highest signal-to-noise ratio as a benchmark, and combines installation height and instantaneous phase information to determine the initial positioning position and initial phase residual, ensuring high reliability and accuracy of the starting point for iterative optimization and avoiding inefficiency caused by blind iteration. Then, through two iterative corrections, the first is based on the initial residuals of all sensors and preset weights to achieve a significant approximation of the positioning position, and the second uses fine correction coefficients to achieve precise fine-tuning, gradually reducing the phase residual and allowing the positioning position to continuously converge towards the true defect position, balancing optimization efficiency and accuracy. Finally, the final defect positioning position is locked through dual threshold judgment, avoiding the limitations of a single threshold judgment and ensuring the stability and reliability of the results.

[0107] In some embodiments, based on the determined theoretical phase of the wind turbine blades and measured phase Determine the phase residual:

[0108] in, Let be the phase residual of the defect at the radial position r of the blade corresponding to the i-th signal receiving device.

[0109] Based on the phase residuals of wind turbine blades, a weighted residual minimization objective function is established:

[0110] in, For the first The weight of the phase residual of each signal receiving device is usually set according to the signal-to-noise ratio of the corresponding channel of the signal receiving device. The higher the signal-to-noise ratio, the greater the weight.

[0111] Based on the established objective function of minimizing the weighted residuals, the Gauss-Newton method is used to solve it. First, the Jacobian derivative is determined:

[0112] in, Let be the first derivative of the theoretical phase corresponding to the i-th signal receiving device with respect to the radial position r of the blade defect.

[0113] Secondly, establish a Gauss-Newton parameter iterative update mechanism:

[0114] in, This is the updated value of the radial position of the defect obtained after the (k+1)th iteration. This is the current value of the radial position of the defect used in the k-th iteration.

[0115] To achieve iterative parameter updates and algorithm convergence, the following initial values ​​need to be determined:

[0116] in, The installation height represents the signal receiving device with the highest signal-to-noise ratio. The measured phase information represents the signal receiving device with the highest signal-to-noise ratio.

[0117] In some embodiments, the radial distance inversion algorithm described above is used to achieve the optimal solution for the defect distance of the wind turbine blade, and the confidence level of the defect location result is obtained as follows:

[0118] in, The estimated variance of the defect location results for wind turbine blades. This represents the phase noise variance.

[0119] In one example, a wind turbine generator set located in a wind farm in southwestern my country was used for testing and verification to validate the effectiveness of the wind turbine blade defect monitoring method proposed in this invention, and its application in areas such as... Figure 4 The wind turbine generator experimental unit is shown. This example specifically includes the following steps: Step 1: Install a high-frequency loudspeaker at the root of the wind turbine blades to transmit active acoustic excitation signals. At the same time, install a microphone sensor from top to bottom on the inner wall of the wind turbine tower to receive the active acoustic excitation signals transmitted from the blades.

[0120] In this embodiment, the wind turbine tower is 110 meters high and the turbine blades are 95 meters long. A high-frequency loudspeaker is bolted to the center of the blade cover plate and powered by a pitch control unit in the hub. Additionally, 10 microphone sensors are evenly installed along the inner wall of the wind turbine tower from top to bottom, with a 5-meter spacing between them. The microphone sensors are powered by a power cabinet at the bottom of the tower via Ethernet technology, simultaneously transmitting data.

[0121] Step 2: Based on the wind turbine blade rotation speed information, the acoustic excitation signal collected by the microphone sensor is bandpass filtered. Then, based on the fixed frequency information of the active acoustic excitation signal used, the bandpass filtered acoustic excitation signal is further bandstop filtered to obtain acoustic excitation signal components that only contain potential blade fault characteristics.

[0122] Based on the wind turbine blade rotation speed information obtained during the experiment, the acquired acoustic excitation signal was bandpass filtered, with a frequency range of 2900Hz to 3100Hz. After bandpass filtering, further bandstop filtering was performed, with a frequency range of 2950Hz to 3050Hz. After these filtering processes, the acoustic excitation signal component containing only the potential fault characteristics of the blades was obtained.

[0123] Step 3: Construct the corresponding analytical signal using the filtered acoustic excitation signal, and perform instantaneous phase extraction and phase expansion on the analytical signal.

[0124] Step 4: Calculate the phase change of the acoustic excitation signal for each microphone sensor channel, and realize effective monitoring of the blade operating status by constructing a multi-channel fusion anomaly assessment mechanism for wind turbine blades.

[0125] In this embodiment, a total of 10 microphone sensors were installed. A multi-channel fusion anomaly evaluation mechanism was established based on the instantaneous phase unfolding information corresponding to each microphone sensor. The multi-channel fusion anomaly evaluation results during the experiment are as follows: Figure 5 As shown in the figure, significant differences appeared in the multi-channel anomaly assessment results starting from the 2nd second, indicating a change in the operating state of the wind turbine blades and a high probability of local defects. Therefore, the accuracy of the detection results needs to be verified next.

[0126] Step 5: Establish a forward propagation model for radial localization of blade defects by using the physical relationship between the phase change and path change of the acoustic excitation signal.

[0127] Step 6: Utilize the radial distance inversion algorithm to achieve the optimal solution for the defect distance of the wind turbine blade, and simultaneously obtain the confidence estimate of the defect location result, thereby finally determining the defect location information of the wind turbine blade.

[0128] To verify the accuracy of the above wind turbine operating status detection results, a weighted residual minimization objective function was established based on the phase residual of the wind turbine blades. The damage location results of the wind turbine blades can be obtained by solving the objective function. Figure 6 As shown, a local defect exists on the wind turbine blade at a distance of 70 meters from the blade root. This is basically consistent with the results obtained from the field inspection conducted later in the experiment, which demonstrates the feasibility and effectiveness of the wind turbine blade defect monitoring and location method based on acoustic phase tracking proposed in this invention in practical engineering applications.

[0129] Based on the above experimental analysis, a comparative analysis of different methods was further conducted, as shown in Table 1. It can be seen that traditional methods all have certain limitations in addressing the practical engineering problem of real-time online monitoring and defect location of wind turbine blades, making it difficult to meet actual engineering needs. In contrast, the wind turbine blade defect monitoring and location method based on acoustic phase tracking provided by this invention can adapt to complex external environments, and the sensor is easy to install, enabling real-time online monitoring and defect location of wind turbine blades.

[0130] Table 1 Performance Comparison of Different Methods

[0131] Figure 7 This application illustrates a device 700 for monitoring defects in wind turbine blades, as provided in an embodiment of this application. The wind turbine blades are equipped with signal transmitting devices, and the wind turbine tower is equipped with multiple signal receiving devices. The device may include: The acquisition module 701 is used to acquire the operating information of the wind turbine and receive multiple acoustic excitation signals sent by the signal transmitting device through multiple signal receiving devices. The operating information includes blade length, blade rotation angular velocity and signal transmission frequency of the signal transmitting device. The filtering module 702 is used to filter the multiple acoustic excitation signals received by each signal receiving device according to the blade length, angular velocity and signal transmission frequency, so as to obtain multiple fault characteristic signals corresponding to each signal receiving device. The determining module 703 is used to determine the instantaneous phase information of each signal receiving device based on the multiple fault characteristic signals corresponding to each signal receiving device; The determining module 703 is further configured to determine the target instantaneous phase drift rate of the wind turbine blade based on multiple instantaneous phase information, and to determine the defect monitoring result of the wind turbine based on the target instantaneous phase drift rate and a preset detection threshold.

[0132] In some embodiments, the signal transmitting device is located at the center of the blade root cover plate, and the transmitting surface of the signal transmitting device faces the blade cavity; the plurality of signal receiving devices are installed at intervals from top to bottom along the inner wall of the wind turbine tower, and the signal receiving devices are located on the main windward side of the tower.

[0133] In some embodiments, the determining module 703 is further configured to determine the upper limit value and the lower limit value of the frequency change of the acoustic excitation signal according to the blade length, angular velocity and signal transmission frequency, respectively, and to determine the frequency change range according to the upper limit value and the lower limit value of the frequency change; The filtering module 702 is further configured to perform bandpass filtering on the multiple acoustic excitation signals corresponding to each signal receiving device according to the frequency change range to obtain multiple first filtered signals; The determining module 703 is further configured to determine the upper limit of frequency suppression and the lower limit of frequency suppression based on the signal transmission frequency and the preset half-bandwidth parameter, and to determine the frequency suppression range of the acoustic excitation signal based on the upper limit of frequency suppression and the lower limit of frequency suppression. The filtering module 702 is also used to perform band-stop filtering on multiple first filtered signals corresponding to each signal receiving device according to the frequency suppression interval to obtain multiple fault characteristic signals.

[0134] In some embodiments, the device 700 for monitoring defects in wind turbine blades may further include: The determining module 703 is further configured to perform phase offset operations on the multiple fault characteristic signals corresponding to each signal receiving device to obtain the corresponding imaginary part signal, and determine the instantaneous phase value of the corresponding fault characteristic signal based on the fault characteristic signal and the imaginary part signal; The correction module is used to correct the next instantaneous phase value according to a preset correction value when the difference between adjacent instantaneous phase values ​​of the fault characteristic signal exceeds a set threshold, so as to obtain continuous instantaneous phase information of each signal receiving device.

[0135] In some embodiments, the device 700 for monitoring defects in wind turbine blades may further include: The determining module 703 is also used to determine the instantaneous phase drift rate based on each instantaneous phase information; The fusion module is used to fuse the instantaneous phase drift rates of all signal receiving devices to obtain the target instantaneous phase drift rate of the wind turbine blades.

[0136] In some embodiments, the acquisition module 701 can also be used to acquire the theoretical phase information of the theoretical defect feature signals of different defect locations corresponding to each signal receiving device; The determining module 703 is further configured to determine the defect location based on the phase residual corresponding to each signal receiving device and a preset weight, so that the weighted sum of all phase residuals at the defect location is less than a set threshold; the phase residual is the difference between the instantaneous phase information and the theoretical phase information of the defect location.

[0137] In some embodiments, the determining module 703 is further configured to determine the initial positioning position based on the installation height of the signal receiving device with the highest signal-to-noise ratio and instantaneous phase information, and to determine the initial phase residual of the initial positioning position; The determining module 703 is further configured to determine the first positioning position based on the initial phase residual, the preset weight and the initial positioning position, and to determine the first phase residual of the first positioning position; The determining module 703 is further configured to determine the second positioning position based on the first phase residual, the preset weight and the first positioning position, and to determine the second phase residual of the second positioning position; The determining module 703 is further configured to determine the second positioning position as the defect positioning position when the difference between the first positioning position and the second positioning position is less than a first set threshold, or the weighted sum of the second phase residuals of the second positioning position is less than a second set threshold.

[0138] Figure 7 The various modules in the illustrated device can achieve Figure 1 The various steps involved, and the corresponding technical effects achieved, will not be elaborated upon here for the sake of brevity.

[0139] Figure 8 A schematic diagram of the hardware structure of the terminal device provided in an embodiment of this application is shown.

[0140] The terminal device may include a processor 801 and a memory 802 storing computer program instructions.

[0141] Specifically, the processor 801 may include a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits that can be configured to implement the embodiments of this application.

[0142] Memory 802 may include mass storage for data or instructions. For example, and not limitingly, memory 802 may include a hard disk drive (HDD), floppy disk drive, flash memory, optical disk, magneto-optical disk, magnetic tape, or Universal Serial Bus (USB) drive, or a combination of two or more of these. In one instance, memory 802 may include removable or non-removable (or fixed) media, or memory 802 may be non-volatile solid-state memory. Memory 802 may be internal or external to the integrated gateway disaster recovery device.

[0143] In one instance, memory 802 may include read-only memory (ROM), random access memory (RAM), disk storage media device, optical storage media device, flash memory device, electrical, optical, or other physical / tangible memory storage device. Thus, typically, memory includes one or more tangible (non-transitory) computer-readable storage media (e.g., memory devices) encoded with software including computer-executable instructions, and when the software is executed (e.g., by one or more processors), it is operable to perform the operations described with reference to the method for monitoring wind turbine blade defects according to this disclosure.

[0144] The processor 801 reads and executes computer program instructions stored in the memory 802 to achieve... Figure 1 The method for monitoring defects in wind turbine blades in the illustrated embodiment.

[0145] In one example, the terminal device may also include a communication interface 803 and a bus 804. Wherein, for example... Figure 8 As shown, the processor 801, memory 802, and communication interface 803 are connected through bus 804 and complete communication with each other.

[0146] The communication interface 803 is mainly used to realize communication between various modules, devices, units and / or equipment in the embodiments of this application.

[0147] Bus 804 includes hardware, software, or both, that couples components of an end device together. For example, and not as a limitation, the bus may include an Accelerated Graphics Port (AGP) or other graphics bus, an Extended Industry Standard Architecture (EISA) bus, a Front Side Bus (FSB), a Hyper Transport (HT) interconnect, an Industry Standard Architecture (ISA) bus, an Infinite Bandwidth Interconnect, a Low Pin Count (LPC) bus, a memory bus, a Microchannel Architecture (MCA) bus, a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, a Serial Advanced Technology Attachment (SATA) bus, a Video Electronics Standards Association Local (VLB) bus, or other suitable buses, or combinations of two or more of these. Where appropriate, bus 804 may include one or more buses. Although specific buses are described and illustrated in embodiments of this application, this application contemplates any suitable bus or interconnect.

[0148] Furthermore, in conjunction with the wind turbine blade defect monitoring method in the above embodiments, this application embodiment can provide a computer storage medium for implementation. The computer storage medium stores computer program instructions; when these computer program instructions are executed by a processor, they implement any of the wind turbine blade defect monitoring methods in the above embodiments.

[0149] This application also provides a computer program product, including a computer program that, when executed, implements any of the methods for monitoring wind turbine blade defects described in the above embodiments.

[0150] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0151] The functional blocks shown in the above block diagram can be implemented as hardware, software, firmware, or a combination thereof. When implemented in hardware, they can be, for example, electronic circuits, application-specific integrated circuits (ASICs), appropriate firmware, plug-ins, function cards, etc. When implemented in software, the elements of this application are programs or text segments used to perform the required tasks. Programs or text segments can be stored on a machine-readable medium or transmitted over a transmission medium or communication link via data signals carried on a carrier wave. "Machine-readable medium" can include any medium capable of storing or transmitting information. Examples of machine-readable media include electronic circuits, semiconductor memory devices, read-only memory (ROM), flash memory, erasable read-only memory (EROM), floppy disks, compact disc read-only memory (CD-ROM), optical disks, hard disks, fiber optic media, radio frequency (RF) links, etc. Text segments can be downloaded via computer networks such as the Internet, intranets, etc.

[0152] It should also be noted that the exemplary embodiments mentioned in this application describe methods or systems based on a series of steps or apparatus. However, this application is not limited to the order of the above steps; that is, the steps can be performed in the order mentioned in the embodiments, or in a different order, or several steps can be performed simultaneously.

[0153] The aspects of this disclosure have been described above with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block in the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus to produce a machine such that these instructions, executable via the processor of the computer or other programmable data processing apparatus, enable the implementation of the functions / actions specified in one or more blocks of the flowchart illustrations and / or block diagrams. Such a processor can be, but is not limited to, a general-purpose processor, a special-purpose processor, a special application processor, or a field-programmable logic circuit. It is also understood that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can also be implemented by special-purpose hardware performing the specified functions or actions, or can be implemented by a combination of special-purpose hardware and computer instructions.

[0154] The above are merely specific embodiments of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for monitoring defects in wind turbine blades, characterized in that, The wind turbine blades are equipped with signal transmitting devices, and the wind turbine tower is equipped with multiple signal receiving devices. The method includes: The system acquires the operating information of the wind turbine and receives multiple acoustic excitation signals sent by the signal transmitting device through multiple signal receiving devices. The operating information includes blade length, blade rotation angular velocity, and signal transmission frequency of the signal transmitting device. Based on the blade length, angular velocity, and signal transmission frequency, the multiple acoustic excitation signals received by each signal receiving device are filtered to obtain multiple fault characteristic signals corresponding to each signal receiving device. The instantaneous phase information of each signal receiving device is determined based on the multiple fault characteristic signals corresponding to each signal receiving device; The target instantaneous phase drift rate of the wind turbine blade is determined based on multiple instantaneous phase information, and the defect monitoring result of the wind turbine is determined based on the target instantaneous phase drift rate and a preset detection threshold.

2. The method for monitoring defects in wind turbine blades according to claim 1, characterized in that, The signal transmitting device is located at the center of the blade root cover plate, and the transmitting surface of the signal transmitting device faces the blade cavity; the multiple signal receiving devices are installed at intervals from top to bottom along the inner wall of the wind turbine tower, and the signal receiving devices are located on the main windward side of the tower.

3. The method for monitoring defects in wind turbine blades according to claim 1, characterized in that, The filtering process, based on the blade length, angular velocity, and signal transmission frequency, of the multiple acoustic excitation signals corresponding to each signal receiving device yields multiple fault characteristic signals corresponding to each signal receiving device, including: The upper and lower limits of the frequency variation of the acoustic excitation signal are determined based on the blade length, angular velocity, and signal transmission frequency, and the frequency variation range is determined based on the upper and lower limits of the frequency variation. According to the frequency variation range, bandpass filtering is performed on the multiple acoustic excitation signals corresponding to each signal receiving device to obtain multiple first filtered signals; The upper limit and lower limit of frequency suppression are determined based on the signal transmission frequency and the preset half-bandwidth parameter, respectively, and the frequency suppression range of the acoustic excitation signal is determined based on the upper limit and lower limit of frequency suppression. According to the frequency suppression interval, band-stop filtering is performed on multiple first filtered signals corresponding to each signal receiving device to obtain multiple fault characteristic signals.

4. The method for monitoring defects in wind turbine blades according to claim 1, characterized in that, The step of determining the instantaneous phase information of each signal receiving device based on the multiple fault characteristic signals corresponding to each signal receiving device includes: For each signal receiving device, a phase shift operation is performed on the multiple fault characteristic signals to obtain the corresponding imaginary part signal, and the instantaneous phase value of the corresponding fault characteristic signal is determined based on the fault characteristic signal and the imaginary part signal. When the difference between adjacent instantaneous phase values ​​of a fault characteristic signal exceeds a set threshold, the next instantaneous phase value is corrected according to a preset correction value to obtain continuous instantaneous phase information for each signal receiving device.

5. The method for monitoring defects in wind turbine blades according to claim 1, characterized in that, Determining the target instantaneous phase drift rate of the wind turbine blade based on multiple instantaneous phase information includes: The instantaneous phase drift rate is determined based on each instantaneous phase information. The instantaneous phase drift rates of all signal receiving devices are fused to obtain the target instantaneous phase drift rate of the wind turbine blades.

6. The method for monitoring defects in wind turbine blades according to claim 1, characterized in that, After determining the defect monitoring results of the wind turbine based on the target instantaneous phase drift rate and the preset detection threshold, the method further includes: Obtain the theoretical phase information of the theoretical defect characteristic signals corresponding to different defect locations for each signal receiving device; The defect location is determined based on the phase residual corresponding to each signal receiving device and a preset weight, so that the weighted sum of all phase residuals at the defect location is less than a set threshold; the phase residual is the difference between the instantaneous phase information and the theoretical phase information of the defect location.

7. The method for monitoring defects in wind turbine blades according to claim 1, characterized in that, The step of determining the defect location based on the phase residual corresponding to each signal receiving device and a preset weight, so that the weighted sum of all phase residuals at the defect location is less than a set threshold, includes: The initial positioning position is determined based on the installation height of the signal receiving device with the highest signal-to-noise ratio and the instantaneous phase information, and the initial phase residual of the initial positioning position is also determined. The first positioning position is determined based on the initial phase residual, preset weights, and initial positioning position, and the first phase residual of the first positioning position is determined accordingly. The second positioning position is determined based on the first phase residual, the preset weight, and the first positioning position, and the second phase residual of the second positioning position is determined accordingly. If the difference between the first positioning position and the second positioning position is less than the first set threshold, or if the weighted sum of the second phase residuals of the second positioning position is less than the second set threshold, the second positioning position is determined as the defect positioning position.

8. A device for monitoring defects in wind turbine blades, characterized in that, The blades of the wind turbine are equipped with signal transmitting devices, and the tower of the wind turbine is equipped with multiple signal receiving devices, the devices including: The acquisition module is used to acquire the operating information of the wind turbine and receive multiple acoustic excitation signals sent by the signal transmitting device through multiple signal receiving devices. The operating information includes blade length, blade rotation angular velocity and signal transmission frequency of the signal transmitting device. The filtering module is used to filter the multiple acoustic excitation signals received by each signal receiving device according to the blade length, angular velocity and signal transmission frequency, so as to obtain multiple fault characteristic signals corresponding to each signal receiving device. The determination module is used to determine the instantaneous phase information of each signal receiving device based on the multiple fault characteristic signals corresponding to each signal receiving device; The determination module is also used to determine the target instantaneous phase drift rate of the wind turbine blades based on multiple instantaneous phase information, and to determine the defect monitoring result of the wind turbine based on the target instantaneous phase drift rate and a preset detection threshold.

9. A terminal device, characterized in that, The device includes: a processor and a memory storing computer program instructions; when the processor executes the computer program instructions, it implements the method for monitoring wind turbine blade defects as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer program instructions, which, when executed by a processor, implement the method for monitoring wind turbine blade defects as described in any one of claims 1-7.

11. A computer program product, characterized in that, When the instructions in the computer program product are executed by the processor of the electronic device, the electronic device performs the method for monitoring wind turbine blade defects as described in any one of claims 1-7.

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