Blade damage monitoring method and system of wind generating set

Abstract

The invention provides a blade damage monitoring method and system for a wind generating set. The method comprises the steps that acoustic emission signals of acoustic emission sensors on all blades are obtained; performing feature analysis on the acoustic emission signals to obtain feature values of each blade on a plurality of acoustic emission features; for each acoustic emission feature, analyzing the distribution difference among the feature values of the plurality of blades to obtain the difference degree corresponding to each acoustic emission feature; and determining that at least one blade in the plurality of blades has a damage risk in response to the condition that the difference degree of the plurality of acoustic emission characteristics meets a preset difference condition. The problem that the blade damage is difficult to accurately monitor is solved, the damage risk of the blade can be comprehensively analyzed by considering the multiple acoustic emission characteristics and comparing the characteristic value distribution between the blades, and therefore a more accurate damage monitoring result is provided.

Description

Technical Field

[0001] The present disclosure relates to the field of wind power generation, and more specifically, to a blade damage monitoring method and system for a wind power generator set. Background Art

[0002] With the widespread application of wind turbines, the turbines may need to face various harsh and complex external environments, which may accelerate the damage of the turbine blades. Therefore, higher requirements are placed on the monitoring scheme for blade damage.

[0003] In this regard, in some cases, in the related art, the characteristic frequency band of the fracture mode can be obtained by tensile tests of glass fiber, epoxy resin and composite laminate samples, and the blade failure mode can be divided based on the characteristic frequency band. However, such a method cannot provide a monitoring solution for the damage mode of the blade when the blade is actually running. In addition, the blind source separation (BSS) acoustic emission signal can be used to identify the main crack of the blade and the crack extension state of the blade. However, the signal characteristics based on such a method in blade failure analysis are relatively single and cannot provide accurate blade damage monitoring. Summary of the invention

[0004] In view of the problem that it is difficult to accurately monitor blade damage in the related art, the present disclosure provides a blade damage monitoring method and system for a wind turbine generator set.

[0005] A first aspect of the present disclosure provides a blade damage monitoring method for a wind turbine generator set, wherein the wind turbine generator set comprises a plurality of blades, each blade being provided with an acoustic emission sensor, wherein the blade damage monitoring method comprises: acquiring an acoustic emission signal of the acoustic emission sensor on each blade; performing feature analysis on the acoustic emission signal to obtain feature values ​​of each blade on a plurality of acoustic emission features; for each acoustic emission feature, analyzing the distribution difference between the feature values ​​of the plurality of blades to obtain a degree of difference corresponding to each acoustic emission feature; in response to the degree of difference of the plurality of acoustic emission features satisfying a preset difference condition, determining that at least one of the plurality of blades has a risk of damage, wherein the preset difference condition indicates that there is an acoustic emission feature among the plurality of acoustic emission features whose degree of difference exceeds a preset value.

[0006] Optionally, the blade damage monitoring method further includes: in response to the degree of difference between the multiple acoustic emission characteristics satisfying the preset difference condition, determining that a blade among the multiple blades that satisfies the preset damage condition is at risk of damage, wherein the preset damage condition characterizes that the characteristic value of the blade in at least one acoustic emission characteristic is the largest among all blades.

[0007] Optionally, the multiple acoustic emission features include multiple signal waveform features, and the signal waveform features characterize waveform characteristic parameters of the acoustic emission signal, wherein the preset damage condition includes: for signal waveform features exceeding a predetermined number, the characteristic value of the blade is the maximum value among the characteristic values ​​of all blades.

[0008] Optionally, the multiple acoustic emission characteristics include multiple frequency band energy characteristics, each of which corresponds to a plurality of frequency bands, and each frequency band energy characteristic characterizes the energy characteristics of a corresponding frequency band, wherein the multiple frequency band energy characteristics include a first energy characteristic and a second energy characteristic, the minimum frequency of the frequency band corresponding to the first energy characteristic is greater than or equal to the maximum frequency of the frequency band corresponding to the second energy characteristic, wherein the preset damage condition includes: satisfying that a characteristic value of the blade on the first energy characteristic is greater than a characteristic value of the blade on the second energy characteristic.

[0009] Optionally, both the first energy characteristic and the second energy characteristic are multiple, wherein the preset damage condition also includes: the sum of the characteristic values ​​of the blade in the first energy characteristic is greater than a first threshold; and the sum of the characteristic values ​​of the blade in the second energy characteristic is less than a second threshold.

[0010] Optionally, the multiple frequency band energy characteristics also include a third energy characteristic, the minimum frequency of the frequency band corresponding to the third energy characteristic is greater than or equal to the maximum frequency of the frequency band corresponding to the first energy characteristic, wherein the preset damage condition also includes: the characteristic value of the blade in the third energy characteristic is not a null value.

[0011] Optionally, the multiple acoustic emission characteristics include multiple signal waveform characteristics and multiple frequency band energy characteristics, the signal waveform characteristics characterize the waveform characteristic parameters of the acoustic emission signal, the multiple frequency band energy characteristics correspond to multiple frequency bands respectively, and each frequency band energy characteristic characterizes the energy characteristics of the corresponding frequency band, wherein the preset difference condition includes: a signal waveform characteristic that satisfies the plurality of signal waveform characteristics and has a difference degree exceeding a first preset value; and a frequency band energy characteristic that satisfies the plurality of frequency band energy characteristics and has a difference degree exceeding a second preset value.

[0012] Optionally, the multiple frequency bands are all included in the full frequency band of the acoustic emission signal, and the frequency band energy characteristics represent the proportion of the energy of the corresponding frequency band in the full frequency band, wherein the degree of difference is the consistency difference between the characteristic values ​​of the multiple blades, and the consistency difference represents the degree of discreteness between the characteristic values.

[0013] Optionally, the blade damage monitoring method further includes: before performing feature analysis on the acoustic emission signal, according to the vibration frequency of the transmission chain of the wind turbine generator set, removing the signal component corresponding to the vibration frequency from the acoustic emission signal to obtain the removed acoustic emission signal.

[0014] A second aspect of the present disclosure provides a blade damage monitoring system for a wind turbine generator set, wherein the wind turbine generator set includes a plurality of blades, and the blade damage monitoring system includes a monitoring platform and an acoustic emission sensor configured on each blade, wherein the monitoring platform includes: a processor; and a memory for storing instructions executable by the processor, wherein when the instructions executable by the processor are executed by the processor, the processor is prompted to execute the blade damage monitoring method for the wind turbine generator set according to the embodiment of the present disclosure.

[0015] Optionally, the wind turbine generator set also includes a main control system, and the monitoring platform sends the execution result of the processor to the main control system, so that the main control system performs a control action corresponding to the execution result, and the control action includes controlling the wind turbine generator set to retract blades and / or shut down.

[0016] A third aspect of the present disclosure provides a computer-readable storage medium. When instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the blade damage monitoring method of a wind turbine generator set according to an embodiment of the present disclosure.

[0017] A fourth aspect of the present disclosure provides a computer program product, comprising computer executable instructions, which, when executed by at least one processor, implement the blade damage monitoring method for a wind turbine generator set according to an embodiment of the present disclosure.

[0018] According to the blade damage monitoring method and system of the wind turbine generator set disclosed in the present invention, the acoustic emission signal on the blade can be acquired, and the characteristic value of the blade on multiple acoustic emission characteristics can be obtained by performing feature analysis on the acoustic emission signal. The distribution difference between the characteristic values ​​of each blade can be analyzed to obtain the difference degree corresponding to each acoustic emission characteristic. Therefore, when the difference degree of the acoustic emission characteristics meets the preset difference condition, it can be determined that at least one of the multiple blades has a damage risk. In this way, by considering multiple acoustic emission characteristics and comparing the distribution of characteristic values ​​between each blade, the damage risk of the blade can be comprehensively analyzed, thereby providing more accurate damage monitoring results and timely blade maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 Schematic diagram showing the configuration positions of acoustic emission sensors in a blade damage monitoring method of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0020] Figure 2 is a schematic flow chart showing a method for monitoring blade damage of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0021] Figure 3 is a schematic flow chart showing the steps of wavelet packet denoising in a method for monitoring blade damage of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0022] Figure 4 is a comparison diagram showing acoustic emission signals before and after wavelet packet denoising in a blade damage monitoring method for a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0023] Figure 5 is a comparison diagram showing acoustic emission signals before and after filtering in a blade damage monitoring method for a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0024] Figure 6 is a schematic flow chart showing an acoustic emission feature processing method in a blade damage monitoring method of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0025] Figure 7 1 is a schematic diagram showing a waveform representation of an acoustic emission signal during a blade damage process in a blade damage monitoring method for a wind turbine generator set according to an exemplary embodiment of the present disclosure.

[0026] Figure 8 is a schematic flow chart showing an example of a method for monitoring blade damage of a wind turbine generator system according to an exemplary embodiment of the present disclosure.

[0027] Fig. 9 is a schematic block diagram showing a blade damage monitoring system of a wind turbine generator system according to an exemplary embodiment of the present disclosure. DETAILED DESCRIPTION

[0028] The following specific embodiments are provided to help the reader obtain a comprehensive understanding of the methods, devices and / or systems described herein. However, after understanding the disclosure of the present application, various changes, modifications and equivalents of the methods, devices and / or systems described herein will be clear. For example, the order of operations described herein is only an example and is not limited to those orders set forth herein, but can be changed as will be clear after understanding the disclosure of the present application, except for operations that must occur in a specific order. In addition, for greater clarity and simplicity, the description of features known in the art may be omitted.

[0029] The features described herein can be implemented in different forms and should not be construed as being limited to the examples described herein. Rather, the examples described herein have been provided to illustrate only some of the many possible ways to implement the methods, devices, and / or systems described herein, which will be clear after understanding the disclosure of the present application.

[0030] As used herein, the term "and / or" includes any one of the associated listed items and any combination of any two or more.

[0031] Although terms such as "first", "second", and "third" may be used herein to describe various members, components, regions, layers, or portions, these members, components, regions, layers, or portions should not be limited by these terms. Instead, these terms are only used to distinguish one member, component, region, layer, or portion from another member, component, region, layer, or portion. Therefore, without departing from the teachings of the examples described herein, the first member, first component, first region, first layer, or first portion referred to in the examples may also be referred to as the second member, second component, second region, second layer, or second portion.

[0032] In the specification, when an element (such as a layer, a region, or a substrate) is described as being “on”, “connected to”, or “coupled to” another element, the element may be directly “on”, “connected to”, or “coupled to” another element, or one or more other elements may be present therebetween. Conversely, when an element is described as being “directly on”, “directly connected to”, or “directly coupled to” another element, there may be no other elements present therebetween.

[0033] The terms used herein are only used to describe various examples and are not intended to limit the disclosure. Unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. The terms "comprise", "include" and "have" indicate the presence of the described features, quantities, operations, components, elements and / or combinations thereof, but do not exclude the presence or addition of one or more other features, quantities, operations, components, elements and / or combinations thereof.

[0034] Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as those generally understood by a person of ordinary skill in the art to which the present disclosure belongs after understanding the present disclosure. Unless explicitly defined as such herein, terms (such as those defined in a general dictionary) should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and the present disclosure, and should not be interpreted in an idealized or overly formal manner.

[0035] Furthermore, in the description of examples, when it is considered that a detailed description of a well-known related structure or function would cause vague interpretation of the present disclosure, such a detailed description will be omitted.

[0036] Acoustic emission monitoring technology is a non-destructive testing method that can monitor and record acoustic wave signals in real time and evaluate the health of the structure by analyzing the signals. For example, when the blades of a wind turbine are affected by external factors or suffer internal damage, specific stress wave signals will be generated. By analyzing and comparing these signals, it can be determined whether there are potential problems with the blades.

[0037] Acoustic emission sensors can be used to monitor wind turbine blade damage in the early and mid-term. Acoustic emission monitoring technology detects the sound wave signals generated inside the material to determine whether there is potential damage to the structure. For wind turbine blades, this technology can help monitor the structural integrity of the blades and detect any possible defects or damage in a timely manner.

[0038] In view of the problems described above, the present disclosure provides a blade damage monitoring method, system, computer-readable storage medium and computer program product for a wind turbine generator set to solve or at least alleviate the problem of being unable to accurately monitor blade damage in related technologies.

[0039] According to a first aspect of an exemplary embodiment of the present disclosure, a method for monitoring blade damage of a wind turbine is provided, wherein the wind turbine comprises a plurality of blades, and an acoustic emission sensor is disposed on each blade.

[0040] Figure 1 FIG. 2 shows an exemplary configuration position of an acoustic emission sensor on a blade according to an exemplary embodiment of the present disclosure. Figure 1 As shown, the wind turbine generator set may include three blades, and an acoustic emission sensor may be configured at the position where the risk of damage to the trailing edge of each blade is the greatest. For example, the acoustic emission sensor may be configured within a range of 15 to 30 meters from the blade tip, for example, at a position 22 meters from the blade tip. The acoustic emission sensor may be configured within the above-mentioned position range on the leading edge and / or trailing edge of the blade. The acoustic emission sensor may be attached to the leeward surface (SS surface) skin of the blade to measure longitudinal waves, transverse waves and mixed waves. These acoustic emission sensors may constitute an acoustic emission system for the blade, and the sampling frequency of each acoustic emission sensor may be, for example, 1.67 MHz, wherein 2 seconds of acoustic emission data are stored every 10 seconds. As an example, the configuration position of the acoustic emission sensor on each blade may be the same, but it is not limited thereto, and the configuration position of the acoustic emission sensor on different blades may also be different, and one or more acoustic emission sensors may be configured on each blade.

[0041] Although the configuration position of the acoustic emission sensor is described here by taking the position of the trailing edge SS surface skin 22 meters away from the blade tip as an example, the embodiments of the present disclosure are not limited thereto. For example, the configuration position of the acoustic emission sensor may also be the position of the leading edge windward surface (PS surface) 20 meters away from the blade root. In addition, in the embodiments of the present disclosure, various combinations of configuration positions and distances of the acoustic emission sensors, the number of sensors, and the installation process of the sensors may all be changed.

[0042] Based on the configured acoustic emission sensor, the blade damage monitoring method may include the following steps:

[0043] like Figure 2 As shown, in step S210, the acoustic emission signal of the acoustic emission sensor on each blade may be acquired.

[0044] Specifically, during the operation of the wind turbine generator set, the acoustic emission signals can be collected by the acoustic emission sensors arranged on each blade.

[0045] As an example, after the acoustic emission signal is acquired, the acoustic emission signal may be preprocessed to obtain a processed signal for subsequent feature analysis.

[0046] In one example, the pre-processing of the acoustic emission signal may include wavelet packet noise reduction processing.

[0047] Specifically, when monitoring blades in a field environment, external factors such as the field environment have a greater impact on the collected acoustic emission signals, making the collected acoustic emission signals more complex, resulting in a lot of noise signals mixed in the acoustic emission signals. Due to the existence of these noise signals, the signal processing has a greater impact, thereby reducing the signal-to-noise ratio of the acoustic emission signal. In addition, the noise reduction processing of the acoustic emission signal can improve the accuracy of the subsequent feature signal extraction and the qualitative characterization of the feature signal, thereby improving the signal-to-noise ratio of the acoustic emission signal.

[0048] Wavelet packet denoising (Noise Reduction by Wavelet Packet Transform) is a commonly used signal processing method, which is suitable for removing noise from signals. This method is an algorithm developed based on wavelet analysis, which can effectively reduce the noise in the signal and retain the important information of the signal. The wavelet packet denoising method can decompose the signal into multiple frequency bands, then select the appropriate frequency band for noise reduction processing, and finally reconstruct the processed signal. This method can suppress noise to different degrees in different frequency bands, thereby improving the noise reduction effect.

[0049] As an example, wavelet packet denoising can be performed in the following way:

[0050] like Figure 3 As shown, in step S310, the wavelet basis function and the number of decomposition levels may be screened.

[0051] Specifically, the wavelet packet denoising process may be a process based on a wavelet analysis method, wherein wavelet transform may be used to perform denoising on the acoustic emission signal.

[0052] Here, the object of wavelet packet denoising is the acoustic emission signal of the blade composite material during the degradation process. For example, the dB5 wavelet packet basis function (Daubechies 5Wavelet Basis Function) can be selected to perform 7-layer wavelet packet decomposition on the acoustic emission signal, and the denoising program completed by the Python language and the wavelet analysis module package wavelet therein performs wavelet denoising on the acoustic emission signal.

[0053] In step S320, wavelet packet decomposition may be performed on the acoustic emission signal.

[0054] Specifically, the acoustic emission signal can be projected into the space spanned by the wavelet packet basis functions described above, and the details of the input signal can be analyzed through multiple iterations of wavelet transform. The wavelet packet can be represented by an analysis tree to achieve wavelet packet decomposition of the signal.

[0055] In step S330, the wavelet coefficients of different layers may be processed.

[0056] In the wavelet threshold denoising method, it can be considered that the noise exists in the high-frequency components. For the wavelet coefficients of each layer, the value corresponding to the noise is small. Therefore, a suitable threshold can be selected to set the wavelet coefficients whose absolute value is less than the preset coefficient threshold to 0, and the coefficients with larger absolute values ​​can be retained or shrunk. In this way, a noise processing method that compromises soft and hard thresholds can be achieved.

[0057] In step S340, the noise reduction signal may be reconstructed by inverse wavelet transform.

[0058] In this step, inverse wavelet transform can be used for reconstruction, that is, a denoised signal is obtained.

[0059] The relevant parameters in the wavelet threshold denoising method selected in the above process can be used to denoise the acoustic emission data fragments collected by a certain unit. The effect before and after wavelet packet denoising is compared. Figure 4 As shown, from Figure 4 It can be seen that the wavelet threshold denoising method can effectively eliminate the interference of noise signals in the experiment and retain the effective components in the signal.

[0060] Although the wavelet packet decomposition denoising method is described as an example here, the embodiments of the present disclosure are not limited to this. Alternatively or additionally, other denoising methods for acoustic emission signals may be selected, such as a denoising method based on empirical mode decomposition (EMD) and correlation coefficient principle.

[0061] In another example, the pre-processing of the acoustic emission signal may include a filtering process.

[0062] Specifically, the blade damage monitoring method may further include: before performing feature analysis on the acoustic emission signal, according to the vibration frequency of the transmission chain of the wind turbine generator set, removing the signal component corresponding to the vibration frequency from the acoustic emission signal to obtain the acoustic emission signal after removal.

[0063] The filtering process can be performed after the wavelet packet noise reduction process. Here, the acoustic emission signal after the wavelet packet noise reduction process may contain vibration frequency components corresponding to the transmission chain of the unit, for example, there may be low-frequency components of 900 Hz and its multiples. These low-frequency components can be preliminarily judged as frequency components belonging to the transmission chain. Therefore, it is necessary to perform high-pass filtering on the acoustic emission signal to avoid the sound frequency components caused by other components in the unit affecting the blade monitoring method of the embodiment of the present disclosure. As an example, a Butterworth high-pass filter (with a cutoff frequency of 200 kHz) can be used to perform the above filtering process. The filtered time domain signal is as follows: Figure 5 As shown in Figure 2, the filtered acoustic emission signal only retains the high-frequency components in the signal. Figure 5 A comparison diagram of acoustic emission signals before and after filtering in a blade damage monitoring method for a wind turbine generator system according to an exemplary embodiment of the present disclosure is shown.

[0064] In step S220, feature analysis may be performed on the acoustic emission signal to obtain feature values ​​of each blade on multiple acoustic emission features.

[0065] In this step, if Figure 6 As shown, for the acquired acoustic emission signal or the signal after the above preprocessing (for example, the signal after the wavelet packet noise reduction processing S610 and the filtering processing S620), the signal can be feature processed, the characteristics of the signal can be analyzed, and the characteristic value of each blade in each feature can be obtained for subsequent monitoring and diagnosis.

[0066] As an example, the feature analysis may include signal waveform feature analysis S630 and / or frequency band energy feature analysis S640. Accordingly, the plurality of acoustic emission features may include a plurality of signal waveform features and a plurality of frequency band energy features.

[0067] Specifically, for the analysis of signal waveform characteristics, the process of blade damage can be divided into three types: damage initiation, damage extension and fracture. Different stages of damage can be accompanied by obvious changes in acoustic emission signal parameters, thus presenting different signal waveform characteristics. For example, Figure 7 The waveforms of acoustic emission signals at different stages of blade damage are shown. Figure 7 In stage 1), the signal amplitude and energy are low, the duration is short, and the rise is slow; when the blade damage expands ( Figure 7 In stage 2), the signal amplitude and energy are high, the duration is long, and the rise is fast; when the blade material is close to breaking ( Figure 7 In stage 3), the signal amplitude and energy increase dramatically, last longer, and rise fastest.

[0068] The signal waveform characteristics (or also known as "Acoustic Emission Signal Characterization") are used to treat the defect signal of the wind turbine blade composite material as a burst signal. The signal waveform characteristics may include but are not limited to: the energy, amplitude, cumulative number of events, number of impacts, number of ringings, rise time and duration of the acoustic emission. In addition, the signal waveform characteristics that can characterize the continuity of the acoustic emission signal may include the number of ringings, signal level and energy, and the effective value of the voltage.

[0069] In addition, the signal waveform features may also include peak count, average frequency, inverse frequency, initial frequency, signal strength, absolute frequency, local power spectrum, centroid frequency, peak frequency, absolute energy, arrival time, etc. The embodiments of the present disclosure do not impose any particular restrictions on the specific signal waveform features and the frequency band energy features to be described below. In the embodiments of the present disclosure, the combination of various features, various feature quantization methods, and different feature calculation durations may all be changed.

[0070] Through the above-mentioned signal waveform feature analysis, the signal waveform feature (also called "waveform parameter feature") can be obtained, and the features exceeding the preset threshold value can be extracted. The signal waveform feature can characterize the waveform characteristic parameters of the acoustic emission signal. The signal waveform feature can include, for example, the number of ringing times, rise time, duration, energy, peak amplitude, root mean square, etc. Based on such signal waveform features, the blade degradation level can be determined by comparing the acoustic emission parameter feature sizes of the test bench blades and the in-service units.

[0071] For the frequency band energy characteristic analysis, the wavelet energy spectrum coefficient method can be used to calculate the energy of different frequency bands, and the energy of different frequency bands can be spread out in time sequence to determine the blade degradation trend.

[0072] Multiple frequency band energy features may correspond to multiple frequency bands respectively, and each frequency band energy feature characterizes the energy feature of the corresponding frequency band. For example, the full frequency band of the acoustic emission signal may be divided into multiple frequency bands, and these frequency bands may be further divided into multiple sub-frequency bands. In this way, energy feature analysis may be performed for different frequency bands to obtain the energy strength of different frequency bands. These energy strengths reflect the distribution of the acoustic emission signal in different frequency bands, so that whether the blade is damaged and the degree of damage may be evaluated according to the frequency band distribution of the acoustic emission signal.

[0073] As an example, multiple frequency bands may be included in the full frequency band of the acoustic emission signal, and the frequency band energy characteristics may characterize the energy proportion of the corresponding frequency band in the full frequency band. For example, the full frequency band may be divided into a high frequency band, a mid-frequency band, and a low frequency band, and these frequency bands may be divided into multiple sub-frequency bands, and each frequency band may have a corresponding frequency band energy characteristic.

[0074] Multiple frequency band energy characteristics may include a first energy characteristic, a second energy characteristic and a third energy characteristic. The minimum frequency of the frequency band corresponding to the first energy characteristic (for example, corresponding to the mid-frequency band) may be greater than or equal to the maximum frequency of the frequency band corresponding to the second energy characteristic (for example, corresponding to the low-frequency band), and the minimum frequency of the frequency band corresponding to the third energy characteristic (for example, corresponding to the high-frequency band) may be greater than or equal to the maximum frequency of the frequency band corresponding to the first energy characteristic.

[0075] By processing the acoustic emission signal through the above-mentioned signal waveform feature analysis and frequency band energy feature analysis, multiple acoustic emission features can be obtained, for example, including signal waveform features and / or frequency band energy features. The following Table 1 shows 13 example features obtained by processing, but the embodiments of the present disclosure are not limited thereto, and other acoustic emission features can also be selected.

[0076] Table 1

[0077]

[0078] In Table 1 above, f i_j Indicates the jth acoustic emission feature of the i-th blade of the wind turbine generator set, where i can range from 1 to the total number of blades, and j can range from 1 to the total number of acoustic emission features, for example, 1 to 13 in Table 1 above. Here, the jth acoustic emission feature can be a signal waveform feature or a frequency band energy feature.

[0079] In step S230, for each acoustic emission feature, the distribution difference between the feature values ​​of the plurality of blades may be analyzed to obtain the degree of difference corresponding to each acoustic emission feature.

[0080] As described above, the characteristic value of each acoustic emission feature can be calculated for each blade, and the distribution of the characteristic values ​​of each blade can be statistically analyzed in the dimension of each acoustic emission feature, and the distribution differences between the characteristic values ​​can be analyzed. In this way, characteristic values ​​that deviate from the group can be found, and the blades corresponding to such characteristic values ​​are at greater risk of damage.

[0081] The degree of difference can be represented by a statistical value of the distribution difference between the characteristic values ​​of the leaves. As an example, the degree of difference can be the consistency difference between the characteristic values ​​of multiple leaves, and the consistency difference represents the degree of dispersion between the characteristic values. The method of measuring the consistency difference can include calculation methods such as standard deviation and variance, and accordingly, the degree of difference can be represented by statistics such as standard deviation and variance.

[0082] In addition to the above-mentioned statistics such as standard deviation and variance, in the embodiments of the present disclosure, an example of calculating consistency difference is given.

[0083] Specifically, for each acoustic emission feature, the degree of difference can be determined based on the maximum and minimum values ​​of the characteristic values ​​of each blade. For example, referring to the representation in Table 1, taking a three-blade unit as an example, assuming that for the same position measurement point, the j-th acoustic emission signal feature of the i-th blade is expressed as f i_j The consistency difference ε of the three-blade eigenvalue on the jth acoustic emission feature can be calculated using the following formula (1): j :

[0084]

[0085] The value range of j can be 1 to the total number of acoustic emission features. For example, taking Table 1 as an example, j can be 1, 2, ..., 13.

[0086] In step S240, in response to the difference between the plurality of acoustic emission characteristics satisfying a preset difference condition, it may be determined that at least one of the plurality of blades has a risk of damage.

[0087] Here, the preset difference condition may represent an acoustic emission feature among the plurality of acoustic emission features, the difference of which exceeds a preset value.

[0088] In this step, in one example, the preset difference condition may include: (1) a signal waveform feature satisfying that a difference degree among multiple signal waveform features exceeds a first preset value; and (2) a frequency band energy feature satisfying that a difference degree among multiple frequency band energy features exceeds a second preset value.

[0089] Here, the first preset value and the second preset value can be set according to actual needs, for example, both can be 0.5.

[0090] As an example, for each signal waveform feature, it can be determined whether the maximum value of the characteristic values ​​of each blade exceeds the first preset value, and for each frequency band energy feature, it can be determined whether the maximum value of the characteristic values ​​of each blade exceeds the second preset value. For example, the above preset difference condition can be expressed by the following formula (2):

[0091]

[0092] Wherein, [1,n] represents the serial number of the signal waveform feature, and [n+1,m] represents the serial number of the frequency band energy feature. Taking Table 1 above as an example, the preset difference condition can be: max(ε j )>0.5, j is 1, 2…, 5; and max(ε j )>0.5, j is 6, 7,…,13.

[0093] However, the preset difference condition is not limited thereto. In another example, the judgment may be made based only on the signal waveform characteristics or the frequency band energy characteristics. Specifically, the preset difference condition may include: the above-mentioned condition (1) or condition (2).

[0094] In response to the degree of difference between the acoustic emission characteristics satisfying the above-mentioned preset difference condition, it can be determined that at least one of the multiple blades is at risk of damage; in response to the degree of difference between the acoustic emission characteristics not satisfying the above-mentioned preset difference condition, it can be considered that the risk of blade damage is small.

[0095] Specifically, the degree of discreteness of the characteristic values ​​can be determined by analyzing the distribution of the characteristic values ​​of each blade. Since the characteristic values ​​of damaged blades may deviate from or even seriously deviate from the characteristic values ​​of normal blades, the greater the degree of discreteness, the greater the possibility that these blades contain damaged blades. In this way, by comparing the characteristic values ​​between blades, it is possible to quickly determine that the unit may currently have damaged blades, so that timely maintenance or processing can be carried out.

[0096] The above describes the process of determining whether there is a damaged blade among all blades, so that early warning or maintenance can be performed. However, the embodiments of the present disclosure are not limited thereto, and the embodiments of the present disclosure can also specifically locate the damaged blade.

[0097] As an example, the blade damage monitoring method may further include: in response to the difference degree of the multiple acoustic emission characteristics satisfying the preset difference condition, determining that a blade among the multiple blades that meets the preset damage condition has a damage risk. Here, the preset damage condition may indicate that the characteristic value of the blade on at least one acoustic emission characteristic is the largest among all blades.

[0098] Specifically, compared with normal blades or undamaged blades, damaged blades may have more prominent characteristic values ​​of acoustic emission characteristics, such as stronger energy, more ringing times, longer duration, etc. The characteristic value of the acoustic emission characteristic can be positively correlated with the degree of damage to the blade. Therefore, the damaged blade will show the largest characteristic value in at least one acoustic emission characteristic, and this characteristic value obviously deviates from the characteristic value of the normal blade, resulting in a large degree of difference in this characteristic. In this way, based on the above scheme for preliminary judgment of whether there is a damaged blade, the characteristic values ​​of each blade in each acoustic emission characteristic that have been analyzed can be used to further locate the specific damaged blade without introducing a new monitoring or calculation process, thereby realizing a layered progressive monitoring scheme for preliminary warning and specific positioning.

[0099] For preset damage conditions, in one example, as described above, the acoustic emission characteristics may include multiple signal waveform characteristics, and the signal waveform characteristics may characterize the waveform characteristic parameters of the acoustic emission signal. In this example, the preset damage conditions may include: for signal waveform characteristics exceeding a predetermined number, the characteristic value of the blade is the maximum value among the characteristic values ​​of all blades.

[0100] Here, the predetermined number can be set according to actual needs, and it can be a preset fixed value; it can also be determined based on the total number of signal waveform features, for example, it can be a predetermined proportion of the total number of signal waveform features. For example, taking Table 1 above as an example, the total number of signal waveform features is 5 (i.e., features numbered 1 to 5), and the predetermined number can be an integer greater than 1 / 2 of the total number of signal waveform features, such as 3. Here, the predetermined proportion can be set according to actual needs.

[0101] Among all the leaves, if the characteristic value of a certain leaf is the maximum value among all the leaves in more than a predetermined number of signal waveform features, then the leaf can be considered to be a damaged leaf. Taking the above Table 1 as an example, for a certain leaf, if the characteristic value of at least 3 features in the signal waveform features numbered 1-5 is the maximum value among all the leaves, then the leaf is considered to have a risk of damage.

[0102] Since the signal waveform characteristics can reflect the overall waveform characteristics of the acoustic emission signal, the damaged blade can be located only based on the signal waveform characteristics through the above method, making the positioning faster while ensuring the accuracy of monitoring.

[0103] In another example, as described above, the acoustic emission feature may include multiple frequency band energy features, each of which corresponds to a plurality of frequency bands, and each frequency band energy feature may characterize the energy feature of the corresponding frequency band. Here, the multiple frequency band energy features may include a first energy feature and a second energy feature, and the minimum frequency of the frequency band corresponding to the first energy feature is greater than or equal to the maximum frequency of the frequency band corresponding to the second energy feature. The frequency band corresponding to the first energy feature may be, for example, 200kHz to 600kHz, and the frequency band corresponding to the second energy feature may be, for example, 0 to 200kHz.

[0104] The preset damage condition may include: a characteristic value of the blade in the first energy characteristic is greater than a characteristic value of the blade in the second energy characteristic.

[0105] For the blades of wind turbines, when the blades are damaged, the energy of the acoustic emission frequency domain characteristics is mainly concentrated in the medium frequency (for example, 200kHz to 600kHz) frequency range. Therefore, for the divided first energy characteristics and the second energy characteristics, if the first energy characteristic is greater than the second energy characteristic, it can be considered that the energy of the acoustic emission signal is more concentrated in the frequency band corresponding to the first energy characteristic than in the frequency band corresponding to the second energy characteristic. Therefore, there may be a phenomenon of energy concentration in the medium frequency band. Therefore, it can be determined that the blade that meets such conditions is a damaged blade.

[0106] Here, when the frequency band corresponding to the first energy feature and the frequency band corresponding to the second energy feature are both one, the first energy feature and the second energy feature can be directly compared; however, the present disclosure is not limited to this, and the first energy feature and the second energy feature can also correspond to multiple frequency bands, and the first energy feature and the second energy feature can both be multiple.

[0107] For example, multiple first energy features may correspond to multiple sub-bands in 200kHz to 600kHz, respectively; multiple second energy features may correspond to multiple sub-bands in 0kHz to 200kHz, respectively. For example, there may be 4 first energy features, corresponding to 200kHz to 300kHz sub-bands, 300kHz to 400kHz sub-bands, 400kHz to 500kHz sub-bands, and 500kHz to 600kHz sub-bands, respectively; there may be 2 second energy features, corresponding to 0 to 100kHz sub-bands and 100kHz to 200kHz sub-bands, respectively. However, the number of first energy features and second energy features and the way of dividing the frequency bands are not limited thereto, and other frequency bands may also be set as needed.

[0108] In this example, specifically, the preset damage condition may also include: the sum of the characteristic values ​​of the blade in the first energy characteristic is greater than a first threshold; and the sum of the characteristic values ​​of the blade in the second energy characteristic is less than a second threshold.

[0109] Here, the first threshold and the second threshold can be set according to actual needs. For example, the first threshold can be 0.3, and the second threshold can be 0.6. Here, the second threshold can be greater than the first threshold. Through the above conditions, the characteristic values ​​of the blade on multiple first energy characteristics and multiple second energy characteristics can be defined as a whole. When the blade meets the preset damage condition, it can be determined that the blade is damaged; otherwise, it can be considered that the blade is not damaged.

[0110] Taking Table 1 above as an example, the first energy feature can be f i_8 、f i_9 and f i_10 , the second energy characteristic can be f i_6 and f i_7 , the preset damage condition can be expressed by the following formula (3):

[0111]

[0112] When the i-th blade satisfies the above formula (3), it can be considered that the blade is damaged.

[0113] Here, in the case where both the first energy signature and the second energy signature are multiple, the accuracy of locating the damaged blade can be further improved.

[0114] As an example, multiple frequency band energy characteristics may also include a third energy characteristic. The minimum frequency of the frequency band corresponding to the third energy characteristic may be greater than or equal to the maximum frequency of the frequency band corresponding to the first energy characteristic. The frequency band corresponding to the third energy characteristic may be, for example, a high-frequency band. Although as mentioned above, when the blade is damaged, the energy of the acoustic emission frequency domain characteristics is mainly concentrated in the medium-frequency band, and due to the damage to the blade, there may also be energy distribution in the high-frequency band. The energy proportion of the high-frequency band will still be smaller than that of the medium-frequency band, but it will not be a null value.

[0115] In this regard, the preset damage condition may also include: the characteristic value of the blade on the third energy characteristic is not a null value. By setting such a preset damage condition, energy proportion analysis can be performed from different frequency bands, so as to more accurately locate the damaged blade.

[0116] Although an example of automatically locating a damaged blade according to an embodiment of the present disclosure is described above, the process of locating the damaged blade is not limited thereto. When it is determined that a damaged blade exists among all the blades according to an embodiment of the present disclosure, the specific damaged blade may be located by manual inspection or other automated inspection methods.

[0117] Figure 8 FIG. 2 shows an application example of the blade damage monitoring method according to an embodiment of the present disclosure. Figure 8 As shown, in step S801, calculation scheduling can be performed at a predetermined interval (e.g., 10 seconds), and acoustic emission signal data of a predetermined duration (e.g., 2 seconds) is input for each scheduling. In step S802, it can be determined whether the unit is in a power generation condition. If the unit is in a power generation condition, damage monitoring can be performed. In response to the unit not being in a power generation condition, acoustic emission signal data can continue to be acquired; in response to the unit being in a power generation condition, step S803 can be performed.

[0118] In step S803, the acoustic emission signal can be feature analyzed to obtain the feature value of each blade (e.g., blade 1, blade 2, and blade 3) in each acoustic emission feature, for example, the 13 acoustic emission signal features in Table 1 can be processed. In step S804, the consistency difference of the blade in each acoustic emission feature can be calculated to obtain the degree of difference of each acoustic emission feature, and in step S805, it can be determined whether the degree of difference of each acoustic emission feature meets the preset difference condition. In response to meeting the preset difference condition, it is determined that there is a damaged blade in the blade of the unit; in response to not meeting the preset difference condition, the acoustic emission signal data can continue to be acquired to continue monitoring.

[0119] When it is determined that there is a damaged blade among the blades of the unit, in step S806, it can be determined whether each blade meets a preset damage condition to locate the damaged blade and complete blade damage monitoring.

[0120] According to the blade damage monitoring method of the wind turbine generator set in the embodiment of the present disclosure, the acoustic emission monitoring technology can be used to monitor the blade status, and the sound wave signals can be monitored and recorded in real time. By analyzing and comparing these signals, when the risk of blade damage is identified, the blades can be quickly retracted and shut down for protection, thereby effectively reducing the risk of blade breakage caused by the expansion of blade damage and preventing the unit from extreme events such as blade breakage and tower sweeping as much as possible.

[0121] Here, the use of acoustic emission monitoring technology can provide the following advantages: on the one hand, it can achieve continuous monitoring without stopping or removing the blades; on the other hand, it can monitor the entire life of the blades, which helps to predict potential damage development trends, such as the monitoring scenario of the fatigue life development trend of the blades of wind turbines. In addition, this technology can also help optimize maintenance plans, reduce maintenance costs, and improve the reliability and availability of wind turbines.

[0122] In addition, according to the blade damage monitoring method of the wind turbine generator set of the embodiment of the present disclosure, the consistency of the acoustic emission characteristics of the three blades can be compared based on the acoustic emission signal processing characteristics; when the consistency difference of the three blades exceeds the threshold, the wind turbine generator set quickly alarms and shuts down, effectively reducing the risk of blade breakage caused by the expansion of blade damage, and preventing the unit from the extreme event of blade breakage and tower sweeping as much as possible. In this way, real-time online protection of blade damage can be achieved.

[0123] According to a second aspect of an exemplary embodiment of the present disclosure, a blade damage monitoring system for a wind turbine generator set is provided. The wind turbine generator set includes a plurality of blades 10, such as Fig. 9 As shown, the blade damage monitoring system may include a monitoring platform 910 and an acoustic emission sensor 920 configured on each blade. The monitoring platform 910 may include: a processor; a memory for storing processor executable instructions, wherein the processor executable instructions, when executed by the processor, prompt the processor to execute the blade damage monitoring method of the wind turbine generator set according to the embodiment of the present disclosure.

[0124] As an example, the wind turbine generator set also includes a main control system 20, and the monitoring platform 910 can send the execution result of the processor to the main control system 20, so that the main control system 20 performs a control action corresponding to the execution result, and the control action can, for example, include controlling the wind turbine generator set to retract blades and / or shut down. For example, the monitoring platform 910 can include a data collector for receiving a signal from an acoustic emission sensor, and the data collector can send the data of the received signal to the processor.

[0125] Specifically, the acoustic emission sensor 920 can provide access to the acoustic emission data required to calculate all indicators; the monitoring platform 910 can perform relevant logical judgments, including acoustic emission feature processing, difference degree calculation, etc., and output corresponding diagnostic results, control instructions, etc. The main control system 20 can receive the monitoring results from the monitoring platform 910, and can perform actions such as retracting the propeller and shutting down according to the alarm or shutdown instructions corresponding to the monitoring results, thereby realizing real-time online protection of blade damage.

[0126] In addition, the wind farm where the wind turbine generator set is located can also be equipped with an equipment management system, which can centrally manage information such as the status of the acoustic emission sensor, the status of the monitoring platform, and the blade damage status of each unit in the entire wind farm.

[0127] Here, the monitoring platform 910 may include an electronic device, which may include the processor and memory described above to execute the blade damage monitoring method of a wind turbine generator set.

[0128] As an example, the electronic device may be a PC, a tablet device, a personal digital assistant, a smart phone, or other device capable of executing the above instruction set. Here, the electronic device is not necessarily a single electronic device, but may also be any device or circuit capable of executing the above instruction (or instruction set) individually or in combination. The electronic device may also be part of an integrated control system or system manager, or may be configured as a portable electronic device interconnected with a local or remote (e.g., via wireless transmission) interface.

[0129] In electronic devices, the processor may include a central processing unit (CPU), a graphics processing unit (GPU), a programmable logic device, a dedicated processor system, a microcontroller or a microprocessor. As an example and not limitation, the processor may also include an analog processor, a digital processor, a microprocessor, a multi-core processor, a processor array, a network processor, etc.

[0130] The processor can execute instructions or codes stored in the memory, wherein the memory can also store data. Instructions and data can also be sent and received through the network via the network interface device, wherein the network interface device can adopt any known transmission protocol.

[0131] The memory may be integrated with the processor, for example, RAM or flash memory is arranged within an integrated circuit microprocessor or the like. In addition, the memory may include a separate device, such as an external disk drive, a storage array, or any other storage device that can be used by a database system. The memory and the processor may be operatively coupled, or may communicate with each other, such as through an I / O port, a network connection, etc., so that the processor can read files stored in the memory.

[0132] In addition, the electronic device may also include a video display (such as a liquid crystal display) and a user interaction interface (such as a keyboard, a mouse, a touch input device, etc.) All components of the electronic device may be connected to each other via a bus and / or a network.

[0133] According to a third aspect of an embodiment of the present disclosure, a computer-readable storage medium is provided. Specifically, the blade damage monitoring method of a wind turbine generator set according to an embodiment of the present disclosure can be written as a computer program and stored on a computer-readable storage medium. When the instructions in the computer-readable storage medium are executed by at least one processor, the at least one processor is prompted to execute the blade damage monitoring method of the wind turbine generator set according to the exemplary embodiment of the present disclosure. Examples of computer-readable storage media include: read-only memory (ROM), random access programmable read-only memory (PROM), electrically erasable programmable read-only memory (EEPROM), random access memory (RAM), dynamic random access memory (DRAM), static random access memory (SRAM), flash memory, non-volatile memory, CD-ROM, CD-R, CD+R, CD-RW, CD+RW, DVD-ROM, DVD-R, DVD+R, DVD-RW, DVD+RW, DVD-RAM, BD-ROM, BD-R, BD-R LTH, BD-RE, Blu-ray or optical disk storage, hard disk drive (HDD), solid state drive (SSD), card storage (such as, multimedia card, secure digital (SD) card or extreme digital (XD) card), magnetic tape, floppy disk, magneto-optical data storage device, optical data storage device, hard disk, solid state disk and any other device, any other device is configured to store computer program and any associated data, data files and data structures in a non-transitory manner and provide the computer program and any associated data, data files and data structures to a processor or computer so that the processor or computer can execute the computer program. In one example, the computer program and any associated data, data files and data structures are distributed on a networked computer system so that the computer program and any associated data, data files and data structures are stored, accessed and executed in a distributed manner by one or more processors or computers.

[0134] According to a fourth aspect of an embodiment of the present disclosure, a computer program product is provided. The computer program product includes computer instructions. When the computer instructions are executed by a processor, the blade damage monitoring method of a wind turbine generator set according to an embodiment of the present disclosure can be implemented.

[0135] The specific implementation methods of the present disclosure have been described in detail above. Although some embodiments have been shown and described, those skilled in the art should understand that these embodiments may be modified and varied without departing from the principles and spirit of the present disclosure, the scope of which is defined by the claims and their equivalents. These modifications and variations should also be within the scope of protection of the claims of the present disclosure.

Claims

1. A method for monitoring blade damage of a wind turbine generator set, characterized in that: The wind turbine generator set includes a plurality of blades, each blade is provided with an acoustic emission sensor, wherein the blade damage monitoring method includes: Acquiring acoustic emission signals from acoustic emission sensors on each blade; Performing characteristic analysis on the acoustic emission signal to obtain characteristic values ​​of each blade on multiple acoustic emission characteristics; For each acoustic emission feature, analyzing the distribution difference between the characteristic values ​​of the plurality of blades to obtain a degree of difference corresponding to each acoustic emission feature; In response to the difference degree of the multiple acoustic emission characteristics satisfying a preset difference condition, it is determined that at least one of the multiple blades is at risk of damage, wherein the preset difference condition characterizes that there is an acoustic emission characteristic among the multiple acoustic emission characteristics whose difference degree exceeds a preset value.

2. The blade damage monitoring method according to claim 1, characterized in that: The blade damage monitoring method further comprises: In response to the difference between the plurality of acoustic emission characteristics satisfying the preset difference condition, determining that a blade among the plurality of blades satisfying the preset damage condition has a risk of damage, The preset damage condition indicates that the characteristic value of the blade in at least one acoustic emission characteristic is the largest among all blades.

3. The blade damage monitoring method according to claim 2, characterized in that: The plurality of acoustic emission features include a plurality of signal waveform features, wherein the signal waveform features represent waveform characteristic parameters of the acoustic emission signal. The preset damage condition includes: for signal waveform features exceeding a predetermined number, the characteristic value of the blade is the maximum value among the characteristic values ​​of all the blades.

4. The blade damage monitoring method according to claim 2, characterized in that: The multiple acoustic emission features include multiple frequency band energy features, each of which corresponds to a plurality of frequency bands, and each frequency band energy feature represents an energy feature of a corresponding frequency band. The multiple frequency band energy characteristics include a first energy characteristic and a second energy characteristic, the minimum frequency of the frequency band corresponding to the first energy characteristic is greater than or equal to the maximum frequency of the frequency band corresponding to the second energy characteristic, Wherein, the preset damage condition includes: satisfying that a characteristic value of the blade in the first energy characteristic is greater than a characteristic value of the blade in the second energy characteristic.

5. The blade damage monitoring method according to claim 4, characterized in that: The first energy feature and the second energy feature are both multiple, wherein the preset damage condition further includes: The sum of the characteristic values ​​of the blade in the first energy characteristic is greater than a first threshold; and The sum of the characteristic values ​​of the blade in the second energy characteristic is satisfied to be less than a second threshold.

6. The blade damage monitoring method according to claim 4 or 5, characterized in that: The multiple frequency band energy characteristics further include a third energy characteristic, the minimum frequency of the frequency band corresponding to the third energy characteristic is greater than or equal to the maximum frequency of the frequency band corresponding to the first energy characteristic, Wherein, the preset damage condition also includes: the characteristic value of the blade on the third energy characteristic is not a null value.

7. The blade damage monitoring method according to claim 1, characterized in that: The multiple acoustic emission features include multiple signal waveform features and multiple frequency band energy features, the signal waveform features represent waveform feature parameters of the acoustic emission signal, the multiple frequency band energy features correspond to multiple frequency bands respectively, and each frequency band energy feature represents the energy feature of the corresponding frequency band, The preset difference conditions include: A signal waveform feature that satisfies the requirement that the difference among the plurality of signal waveform features exceeds a first preset value; and It is satisfied that there is a frequency band energy feature among the multiple frequency band energy features whose difference degree exceeds a second preset value.

8. The blade damage monitoring method according to claim 7, characterized in that: The multiple frequency bands are all included in the full frequency band of the acoustic emission signal, and the frequency band energy characteristics represent the energy proportion of the corresponding frequency band in the full frequency band. The difference degree is the consistency difference between the characteristic values ​​of the multiple leaves, and the consistency difference represents the degree of discreteness between the characteristic values.

9. The blade damage monitoring method according to claim 1, characterized in that: The blade damage monitoring method further comprises: Before performing feature analysis on the acoustic emission signal, according to the vibration frequency of the transmission chain of the wind turbine generator set, the signal component corresponding to the vibration frequency is removed from the acoustic emission signal to obtain the acoustic emission signal after removal.

10. A blade damage monitoring system for a wind turbine generator set, characterized in that: The wind turbine generator set includes a plurality of blades, and the blade damage monitoring system includes a monitoring platform and an acoustic emission sensor configured on each blade, wherein the monitoring platform includes: processor; a memory for storing instructions executable by the processor, Wherein, when the processor executable instructions are executed by the processor, the processor is prompted to execute the blade damage monitoring method of a wind turbine generator set according to any one of claims 1 to 9.

11. The blade damage monitoring system according to claim 10, characterized in that: The wind turbine generator set also includes a main control system, and the monitoring platform sends the execution result of the processor to the main control system, so that the main control system performs a control action corresponding to the execution result, and the control action includes controlling the wind turbine generator set to retract blades and / or shut down.

12. A computer-readable storage medium, characterized in that: When the instructions in the computer-readable storage medium are executed by a processor of an electronic device, the electronic device is enabled to execute the blade damage monitoring method for a wind turbine generator set according to any one of claims 1 to 9.

13. A computer program product comprising computer executable instructions, characterized in that: When the computer executable instructions are executed by at least one processor, the blade damage monitoring method of a wind turbine generator set according to any one of claims 1 to 9 is implemented.

Images