CN113090471B - Tower clearance audio monitoring system, method and device of wind generating set - Google Patents
Tower clearance audio monitoring system, method and device of wind generating set Download PDFInfo
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- CN113090471B CN113090471B CN201911334440.XA CN201911334440A CN113090471B CN 113090471 B CN113090471 B CN 113090471B CN 201911334440 A CN201911334440 A CN 201911334440A CN 113090471 B CN113090471 B CN 113090471B
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
- F03D—WIND MOTORS
- F03D17/00—Monitoring or testing of wind motors, e.g. diagnostics
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F05—INDEXING SCHEMES RELATING TO ENGINES OR PUMPS IN VARIOUS SUBCLASSES OF CLASSES F01-F04
- F05B—INDEXING SCHEME RELATING TO WIND, SPRING, WEIGHT, INERTIA OR LIKE MOTORS, TO MACHINES OR ENGINES FOR LIQUIDS COVERED BY SUBCLASSES F03B, F03D AND F03G
- F05B2270/00—Control
- F05B2270/30—Control parameters, e.g. input parameters
- F05B2270/33—Proximity of blade to tower
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02E—REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION
- Y02E10/00—Energy generation through renewable energy sources
- Y02E10/70—Wind energy
- Y02E10/72—Wind turbines with rotation axis in wind direction
Abstract
The disclosure provides a tower clearance audio monitoring system, method and device of a wind generating set. The method comprises the following steps: acquiring time domain audio data when a blade of a wind generating set sweeps across a tower; performing statistical transformation on the time domain audio data according to periodically changed characteristics of the time domain audio data, wherein the periodically changed characteristics are obtained according to different distances between the blades and the tower barrel when the blades rotate; locating peak positions in the statistically transformed audio data to obtain peak amplitudes; and comparing the peak magnitude to a magnitude threshold to determine whether to issue a tower headroom warning message.
Description
Technical Field
The present disclosure relates to the field of wind power generation technologies, and more particularly, to a tower headroom audio monitoring system, method, and apparatus for a wind turbine generator system.
Background
In the prior art, as for the tower clearance audio monitoring mode, a sensor monitoring mode that a strain sensor is installed on a blade of a wind generating set, the strain sensor is used for measuring the load on the blade, and then the distance from the blade tip to the tower is deduced is adopted to realize the monitoring mode, or a video monitoring mode that an image of the blade tip sweeping the tower is shot, and the distance from the blade tip to the tower is calculated through an image recognition algorithm is adopted to realize the monitoring mode. However, the adoption of the sensor monitoring and video monitoring mode makes the system of the wind generating set more complex and the cost higher.
Disclosure of Invention
Exemplary embodiments of the present disclosure provide a system, method and apparatus for audio monitoring of tower headroom of a wind turbine generator set, which address at least the above technical problems and other technical problems not mentioned above, and which provide the following advantages.
One aspect of the present disclosure is to provide a tower headroom audio monitoring method of a wind turbine generator set, which may include: acquiring time domain audio data when a blade of a wind generating set sweeps a tower; performing statistical transformation on the time domain audio data according to periodically changed characteristics of the time domain audio data, wherein the periodically changed characteristics are obtained according to different distances between the blades and the tower barrel when the blades rotate; locating peak locations in the statistically transformed audio data to obtain peak amplitudes; and comparing the peak amplitude to an amplitude threshold to determine whether to issue a tower headroom warning message.
The step of statistically transforming the time domain audio data may comprise: classifying the time-domain audio data into valid energy points and invalid energy points by comparing an amplitude of each energy point included in the time-domain audio data with a conversion threshold; calculating a statistical value for each energy point according to the effective energy point and the ineffective energy point and a statistical length, thereby obtaining statistically transformed audio data.
Preferably, the step of calculating the statistical value for each energy point may include: and superposing the amplitudes of the effective energy points within the statistical length.
The switching threshold is determined by the amplitude of a predetermined number of extremum energy points included in the time domain audio data.
The statistical length is determined according to a time period of the time domain audio data.
The statistical length is one quarter of the time period of the time domain audio data.
Another aspect of the present disclosure is to provide a tower headroom audio monitoring device of a wind turbine generator set, which may include: the data acquisition module is used for acquiring time domain audio data when a blade of the wind generating set sweeps across a tower; and the data processing module is used for carrying out statistical transformation on the time domain audio data according to the periodically changed characteristics of the time domain audio data, positioning peak positions in the statistically transformed audio data to obtain peak amplitudes, and comparing the peak amplitudes with amplitude thresholds to determine whether to send out a tower clearance alarm message, wherein the periodically changed characteristics are obtained according to different distances between the blades and the tower barrel when the blades rotate.
Another aspect of the present disclosure is to provide a tower headroom audio monitoring system of a wind turbine generator set, which may include a monitoring device and a processor. The monitoring device may be configured to acquire time domain audio data as the blades of the wind turbine generator set sweep across the tower. The processor may be configured to: the method includes the steps of statistically transforming the time domain audio data according to periodically varying characteristics of the time domain audio data, locating peak positions in the statistically transformed audio data to obtain peak amplitudes, and comparing the peak amplitudes with amplitude thresholds to determine whether to issue a tower headroom warning message, wherein the periodically varying characteristics are obtained according to different distances between the blades and the tower when the blades rotate.
According to another exemplary embodiment of the invention, a computer-readable storage medium is provided, in which a computer program is stored, which, when being executed by a processor, carries out the tower headroom audio monitoring method as described above.
Based on the method and the device, the accuracy of the sound waveform is improved by carrying out statistical transformation on the collected sound signal, namely, the irregular peak in the original sound signal is smoothed by utilizing a conversion threshold, the amplitude in the statistical length range is superposed to eliminate the instantaneous peak so as to reduce the difficulty of signal identification, and meanwhile, the statistical length is reasonably selected so that the peak value of the waveform finally obtained through the statistical transformation is synchronous with the peak value of the original sound signal, so that the real-time performance of the signal identification is improved.
Additional aspects and/or advantages of the present general inventive concept will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the general inventive concept.
Drawings
These and/or other aspects and advantages of the present disclosure will become apparent and more readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a flow chart of a tower headroom audio monitoring method of a wind turbine generator set according to an exemplary embodiment of the present disclosure;
fig. 2 is a diagram of a waveform of original audio data according to an exemplary embodiment of the present disclosure;
FIG. 3 shows two configurations of a wind turbine blade;
fig. 4 is a diagram of a waveform of statistically transformed audio data, according to an example embodiment of the present disclosure;
FIG. 5 is a block diagram of a tower headroom audio monitoring device of a wind turbine generator set according to an exemplary embodiment of the present disclosure;
FIG. 6 is a block diagram of a tower headroom audio monitoring system of a wind turbine generator set according to an exemplary embodiment of the present disclosure.
Detailed Description
The following description with reference to the accompanying drawings is provided to assist in a comprehensive understanding of embodiments of the disclosure as defined by the claims and their equivalents. Various specific details are included to aid understanding, but these are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the disclosure. In addition, descriptions of well-known functions and constructions are omitted for clarity and conciseness.
In the present disclosure, terms including ordinal numbers such as "first", "second", etc., may be used to describe various elements, but these elements should not be construed as being limited to only these terms. These terms are only used to distinguish one element from another. For example, a first element could be termed a second element, and vice-versa, without departing from the scope of the present disclosure.
Before describing the embodiments of the present disclosure, terms of the present disclosure are first explained.
The statistical transformation is to extract the characteristics of the original data waveform by using a statistical method so as to find the inherent periodic rule of the data and analyze the data.
The statistical length refers to a sampling length for converting real-time energy data into probability waveform data.
The switching threshold is a limit value for performing two classifications on the real-time waveform.
Hereinafter, according to various embodiments of the present disclosure, an apparatus and a method of the present disclosure will be described with reference to the accompanying drawings.
FIG. 6 is a block diagram of a tower headroom audio monitoring system of a wind turbine generator set according to an exemplary embodiment of the present disclosure.
Referring to FIG. 6, a tower headroom audio monitoring system 600 can include a monitoring device and a processor 605. The monitoring device may be an audio sensor, which may be implemented as a sound receiving device such as a microphone to collect time domain audio data as the blades of the wind turbine generator system sweep across the tower of the wind turbine generator system.
The monitoring device may be an audio sensor arranged on the wind turbine generator system, for example, the audio sensor 601 arranged on the top of the nacelle in fig. 6, or the audio sensor 602 arranged on the tower.
The monitoring device may also be an audio sensor arranged near the wind park, e.g. an audio sensor 603 arranged on the ground near the tower.
The processor 605 utilizes the conversion threshold to smooth the irregular peaks in the original sound signal to improve the accuracy of the sound waveform, utilizes the statistical length to superpose the amplitude within the statistical length range to eliminate the transient peaks to reduce the difficulty of signal identification, and simultaneously ensures that the peak value of the waveform finally obtained through statistical transformation is synchronous with the peak value of the original sound signal to improve the real-time performance of signal identification.
The operations performed by the processor 605 are described in detail below with reference to fig. 1.
FIG. 1 is a flow chart of a method of audio monitoring of tower headroom for a wind turbine generator set according to an exemplary embodiment of the present disclosure.
Referring to fig. 1, in step S101, time domain audio data is acquired as a blade of a wind turbine generator system sweeps across a tower of the wind turbine generator system. Fig. 2 illustrates a waveform diagram of original audio data according to an embodiment. As shown in fig. 2, the audio signal generated as the blade sweeps across the tower is typically composed of many higher frequency sub-waves with different energy amplitudes, which has the significant characteristic of obtaining a periodic variation according to the different distances between the blade and the tower as it rotates. The time period of the time domain audio data is defined as the time that one blade has elapsed while sweeping through the tower.
For example, the sound signal waveform shown in FIG. 2 is generated when three blades of the wind turbine are periodically swept near the tower, wherein signal A represents the sound signal generated when the first blade is swept near the tower, signal B represents the sound signal generated when the second blade is swept near the tower, and signal C represents the sound signal generated when the third blade is swept near the tower. Each group of signals A, B or C is a wave cluster, each wave cluster is composed of a plurality of wavelets, and the wavelets have the characteristic of being unequal in amplitude and period.
Fig. 3 shows two morphologies at the wind park blade to illustrate the complexity of the wavelet frequencies. As shown in FIG. 3, as the blades rotate past the vicinity of the tower, the air flow between the blades and the tower due to friction produces an acoustic signal, a cluster of which is formed by the superposition of wavelets at frequencies f1, f2, and f3, respectively. The wavelet f1 is generated by the friction between the air flow near the blade root and the blade and the tower when the blade rotates, the wavelet f2 is generated by the friction between the air flow near the middle section of the blade and the tower when the blade rotates, and the wavelet f2 is generated by the friction between the air flow near the blade tip and the blade and the tower when the blade rotates. The left diagram of fig. 3 shows the normal clearance state of the tower, the distance between the blade tip and the tower is large, and the right diagram of fig. 3 shows the abnormal clearance state, and the distance between the blade tip and the tower is small. Because the blade is a long and narrow elastic body, the vibration intensity of each part of the blade changes along with the wind speed, the variable pitch angle, the blade damage, the working state of the unit and the like at any time, the audio amplitude of the three wavelets changes. If the sound signal acquired by the microphone is converted into a frequency domain signal by time-frequency conversion, a plurality of wavelets with different frequencies are superposed, and the intensity of the wavelets is random.
Therefore, although the blades generate periodic sound signals when scanning the tower, the amplitude and waveform of the sound signals change at any time, and the processing method of the sound signals in the prior art may have the problems of missing sound signals, unsmooth data, incapability of capturing specific waveforms in real time (i.e. introducing time delay), and the like, thereby causing difficulty in rapidly identifying clusters and calculating the frequency and width of the clusters. That is, the related art method for processing such audio data has problems of a large difficulty in recognition of the sound signal, a low recognition accuracy, and the like.
In addition, in the prior art, the tower clearance is usually judged to be out of limit according to whether the amplitude of the frequency domain signal exceeds a preset threshold value, and the judgment by using a threshold value method is difficult due to the complex wavelet frequency spectrum and uncertainty of the wavelet amplitude.
In the present disclosure, a smoothed waveform curve is generated by statistical transformation, which will be described below, to embody the frequency of a wave cluster and the width of the peaks and valleys of the wave cluster in time.
In step S102, the time domain audio data is subjected to statistical transformation according to the periodically changing characteristics of the time domain audio data. In the present disclosure, based on the periodically varying characteristics of the acquired audio data, a statistically based transformation algorithm (hereinafter referred to as statistical transformation) is used to convert a variable periodic signal into a regular periodic signal, thereby finding a periodic law inherent to the data itself for subsequent data analysis. Specifically, valid energy points and invalid energy points are classified from the audio data by comparing the amplitude of each energy point included in the acquired audio data with a conversion threshold, and then a statistical value for each energy point is calculated according to the valid energy points and the invalid energy points by a statistical length L, thereby obtaining statistically transformed audio data.
The key to the statistical transformation according to the present disclosure is the determination of the transformation threshold and the determination of the statistical length L.
The switching threshold is determined by the amplitude of a predetermined number of extremum energy points included in the audio data. Preferably, the switching threshold may be calculated by averaging the amplitudes of a predetermined number of extreme energy points included in the acquired audio data and then taking 1/3 of the average.
The statistical length is determined according to a time period of the time domain audio data. In one example, the statistical length L is set to one quarter of a time period of the time domain audio data.
However, the above determination method is only exemplary, and the conversion threshold and the statistical length L may also be determined according to design experience and actual requirements of wind power personnel, and the disclosure is not limited thereto.
As an example, the amplitude of the acquired audio data is first classified using a switching threshold. For example, assuming that the amplitudes of energy points included in a set of audio data generated when a blade sweeps across a tower are 0,1, 2, 3, 4, 5, 6, 3, 2, 1, 6, 3, 4, 5, 4, 3, 7, 1,0, a specific number of energy points of an extreme value, which means that the amplitudes of the energy points of the set of audio data are located at the previous specific number of energy points in ascending order, are selected from all the energy points of the audio data. For example, five extreme energy points whose amplitudes are 7, 6, 5, respectively, are selected from all the energy points of the acquired audio data, but the number of extreme energy points is not limited thereto, and the number of extreme energy points to be selected may be set according to actual conditions, device performance, and the like. After the extreme energy point is determined, the conversion threshold value is calculated as ((7 +6+ 5)/5)/3 =2 according to the determination method of the conversion threshold.
After determining the transition threshold, comparing the transition threshold with the amplitude of each energy point in the obtained audio data, when the amplitude of an energy point is greater than the transition threshold, classifying the energy point as an effective energy point and representing the energy point as 1, and when the amplitude of an energy point is less than or equal to the transition threshold, classifying the energy point as an ineffective energy point and representing the energy point as 0, that is, not participating in the subsequent statistical analysis. <xnotran> , 2 , 0 1 {0,0,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0}. </xnotran> That is, each energy point in the acquired audio data may be represented by each corresponding element in the above-described series.
According to an embodiment of the present disclosure, the switching threshold is a limit for determining whether data is valid data, and is used for deciding the smoothness of the statistical transformation waveform. Experiments show that the larger the conversion threshold is, the lower the image amplitude of statistical transformation is, and the smoother the image is. In addition, the switching threshold also affects the real-time performance, and in particular, the larger the switching threshold, the lower the real-time performance.
Next, each energy point (representing the amplitude of the sound signal) in the newly generated series is counted by a statistical length. In the step of calculating the statistical value for each energy point, the amplitudes of the effective energy points within the statistical length are superposed.
Specifically, for each energy point in the audio data, a sum of a current energy point n to an energy point n-L is calculated as a statistical value of the current energy point according to a statistical length L, wherein if n-L <0, a value of 0 is supplemented before the current energy point to satisfy the statistical length.
For example, according to the above-described determination method of the statistical length, the statistical length is set to 5. <xnotran> {0,0,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0} , , 0, (0000) 0, 0 , 00000. </xnotran> <xnotran> {0,0,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0} , , 0, (000) 00, 00000. </xnotran> <xnotran> {0,0,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0} , , 0, (0) 0011, 00011. </xnotran> <xnotran> {0,0,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0} , , 0 , 00111. </xnotran> <xnotran> {0,0,1,1,1,1,1,1,1,0,1,0,1,1,1,1,1,1,0,0} , , 0 , , 01111. </xnotran> In the above manner, a representation of each element that is statistically transformed can be obtained by analogy. The values contained in the representation of each element are added to obtain a corresponding statistical value for each element (i.e. for each energy point). For example, if the first element is represented as 00000, the statistical transformation value of the first element is 0, the second element is represented as 00000, the statistical transformation value of the second element is 0, the fourth element is represented as 00011, the statistical transformation value of the fourth element is 2, and so on, the statistically transformed number series may be obtained, and the waveform of the statistically transformed audio data may be obtained from the statistically transformed number series, as shown in the lower curve of fig. 4. The upper portion of fig. 4 is a waveform diagram of energy point composition denoted by 1, and the waveform shown in fig. 4 is merely exemplary, and the present disclosure is not limited thereto.
According to the embodiment of the disclosure, the energy of each wavelet is overlapped and accumulated by using the sound amplitude in the statistical length range so as to eliminate the short-time transient spike. Experiments prove that the statistical length and the natural frequency of the sound signal have a certain relation, specifically, the longer the statistical length, the worse the real-time performance, and the shorter the statistical length, the better the real-time performance, but the smoothness of the waveform is reduced and the amplitude is reduced. Therefore, a reasonable statistical length can be set based on the above experience.
The statistical length and transition threshold of the present disclosure combine to determine the amplitude of the statistically transformed waveform. Meanwhile, the finally obtained waveform frequency is the frequency at which each blade sweeps the vicinity of the tower and the peak appears at the peak position of the original sound signal (audio data). That is, the statistical transformation approach of the present disclosure guarantees synchronicity between the original signal and the statistically transformed signal.
In step S103, peak positions are located in the statistically transformed audio data to obtain peak amplitudes. As can be seen from the lower curve of fig. 4, the statistically transformed waveform is a smooth waveform curve from which the position of the peak can be quickly located and the peak amplitude corresponding to the peak position is obtained.
As shown in the lower graph of fig. 4, the statistical length L is set to be a quarter of the time period of the time domain audio data. The waveform of the statistically transformed audio data (lower curve) is aligned in time with the peak of the waveform map composed of the effective energy points (upper curve).
At step S104, the peak amplitude is compared to an amplitude threshold to determine whether to issue a tower headroom warning message. And when the peak amplitude exceeds the amplitude threshold value, sending a tower clearance alarm message to a processor of the wind generating set, so that the processor executes tower clearance action according to the message. Otherwise, the tower clearance alarm message is not sent to the processor, namely the wind generating set operates normally. In the present disclosure, the amplitude threshold may be set according to the experience of the designer and the design requirement, and the present disclosure is not limited thereto.
FIG. 5 is a block diagram of a tower headroom audio monitoring device of a wind turbine generator set according to an exemplary embodiment of the present disclosure.
Referring to fig. 5, the tower clearance audio monitoring device 500 of the wind turbine generator set may include a data acquisition module 501 and a data processing module 502. Each module in tower headroom audio monitoring device 500 can be implemented by one or more modules, and the name of the corresponding module can vary depending on the type of module. In various embodiments, some modules in the tower headroom audio monitoring device 500 may be omitted, or additional modules may also be included. Furthermore, modules/elements according to various embodiments of the present disclosure may be combined to form a single entity, and thus the functions of the respective modules/elements may be equivalently performed prior to the combination.
The data acquisition module 501 may acquire audio data as the blades of the wind turbine generator system sweep across the tower of the wind turbine generator system. By way of example, the data acquisition module 501 may acquire audio signals generated by the blades of the wind turbine generator as they sweep across the tower from a monitoring device (e.g., a sound receiving device such as a microphone) installed in the tower of the wind turbine generator. The waveform diagram of the original audio signal is acquired as shown in fig. 2. The audio signal generated as the blade sweeps across the tower is typically composed of many higher frequency sub-waves with different energy amplitudes, and this audio data has the notable feature of being periodically varied according to the different distances between the blade and the tower as it rotates.
The data processing module 502 performs statistical transformation on the acquired audio data according to the periodically changing characteristics of the audio data. In the present disclosure, the data processing module 502 converts the variable periodic signal into a regular periodic signal using a statistical transformation-based method based on the acquired periodically varying characteristics of the audio data, so as to find the inherent periodic regularity of the data itself for subsequent data analysis. Specifically, the data processing module 502 classifies effective energy points and ineffective energy points from the audio data by comparing the amplitude of each energy point included in the acquired audio data with a conversion threshold, and then calculates a statistical value for each energy point by a statistical length L according to the effective energy points and the ineffective energy points, thereby obtaining statistically transformed audio data.
The data processing module 502 may calculate the transition threshold by averaging the amplitudes of a predetermined number of extreme energy points included in the acquired audio data and then taking 1/3 of the average, setting the statistical length L to 1/4 of the time that one blade spends sweeping across the tower. However, the above determination method is only exemplary, and the data processing module 502 may also determine the conversion threshold and the statistical length L according to the design experience and the actual requirement of the wind power personnel, and the disclosure is not limited thereto.
As an example, first, the data processing module 502 classifies the amplitude of the acquired audio data using a conversion threshold. Specifically, a specific number of extreme energy points are selected from all energy points of audio data, where the extreme energy points represent a specific number of energy points preceding in a group of audio data in ascending order of amplitude. And after the extreme value energy point is determined, determining a conversion threshold. Then, the data processing module 502 compares the transition threshold with the amplitude of each energy point in the obtained audio data, and when the amplitude of an energy point is greater than the transition threshold, the energy point may be classified as a valid energy point and represented as 1, and when the amplitude of an energy point is less than or equal to the transition threshold, the energy point may be classified as a invalid energy point and represented as 0, that is, the subsequent statistical analysis is not involved. That is, each energy point in the acquired audio data may be represented by each corresponding element in the above-described series.
Next, the data processing module 502 counts each energy point (representing the amplitude of the sound signal) in the newly generated sequence by a statistical length. Specifically, for each energy point in the audio data, a sum of a current energy point n to an energy point n-L is calculated as a statistical value of the current energy point according to a statistical length L, wherein if n-L <0, a value of 0 is supplemented before the current energy point to satisfy the statistical length. The waveform of the statistically transformed audio data can be obtained from the statistically transformed statistical value, as shown in the lower curve of fig. 4.
The data processing module 502 locates peak positions in the statistically transformed audio data to obtain peak amplitudes. As can be seen from the lower curve of fig. 4, the statistically transformed waveform is a smooth waveform curve from which the data processing module 502 can quickly locate the position of the peak and obtain the peak amplitude corresponding to the peak position.
The data processing module 502 compares the peak amplitude to an amplitude threshold to determine whether to issue a tower headroom warning message. When the peak amplitude exceeds the amplitude threshold, the data processing module 502 sends a tower headroom warning message to the processor of the wind turbine generator set, so that the processor performs a tower headroom action according to the message. Otherwise, the data processing module 502 does not send a tower clearance warning message to the processor, i.e., the wind turbine generator set is operating normally. In the present disclosure, the amplitude threshold may be set according to the experience of the designer and the design requirements, and is not limited herein.
The statistical transformation method in the present disclosure is applicable to the detection operation of any signal with periodic variation, and is particularly suitable for filtering an audio signal for monitoring tower clearance.
The method and the device can quickly and effectively detect regular (periodically-changed) signals, can accurately locate the wave crest of the audio signal, and simultaneously improve the operation efficiency of the processor.
One skilled in the art will appreciate that the present disclosure includes apparatus directed to performing one or more of the operations/steps described in the present disclosure. These devices may be specially designed and manufactured for the required purposes, or they may comprise known devices in general-purpose computers. These devices have stored within them computer programs that are selectively activated or reconfigured. Such a computer program may be stored in a device (e.g., computer) readable medium, including, but not limited to, any type of disk including floppy disks, hard disks, optical disks, CD-ROMs, and magnetic-optical disks, ROMs (Read-Only memories), RAMs (Random Access memories), EPROMs (Erasable Programmable Read-Only memories), EEPROMs (Electrically Erasable Programmable Read-Only memories), flash memories, magnetic cards, or optical cards, or any type of media suitable for storing electronic instructions, and each coupled to a bus. That is, a readable medium includes any medium that stores or transmits information in a form readable by a device (e.g., a computer).
While the present disclosure has been shown and described with reference to exemplary embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the present disclosure as defined by the appended claims and their equivalents.
Claims (10)
1. A tower clearance audio monitoring method for a wind generating set is characterized by comprising the following steps:
acquiring time domain audio data when a blade of a wind generating set sweeps across a tower;
performing statistical transformation on the time domain audio data according to periodically changed characteristics of the time domain audio data, wherein the periodically changed characteristics are obtained according to different distances between the blades and the tower barrel when the blades rotate;
locating peak locations in the statistically transformed audio data to obtain peak amplitudes; and is provided with
Comparing the peak amplitude to an amplitude threshold to determine whether to issue a tower headroom warning message;
wherein the step of statistically transforming the time domain audio data comprises:
classifying the time-domain audio data into valid energy points and invalid energy points by comparing an amplitude of each energy point included in the time-domain audio data with a conversion threshold;
and calculating the statistic value of each energy point according to the statistic length so as to obtain the audio data subjected to statistic transformation.
2. The method of claim 1, wherein the step of calculating the statistics for each energy point comprises:
and superposing the amplitudes of the effective energy points in the statistical length.
3. The method of claim 1, wherein the transition threshold is determined by a magnitude of a predetermined number of extremum energy points included in the time domain audio data.
4. The method of claim 1, wherein the statistical length is determined from a time period of the time domain audio data.
5. The method of claim 1, wherein the statistical length is one quarter of a time period of the time domain audio data.
6. A tower headroom audio monitoring device of a wind generating set, the device comprising:
the data acquisition module is used for acquiring time domain audio data when the blades of the wind generating set sweep through the tower;
a data processing module, configured to perform statistical transformation on the time domain audio data according to a periodically varying characteristic of the time domain audio data, locate a peak position in the statistically transformed audio data to obtain a peak amplitude, and compare the peak amplitude with an amplitude threshold to determine whether to issue a tower headroom warning message, wherein the periodically varying characteristic is obtained according to different distances between the blade and the tower while rotating;
the data processing module is further used for classifying the time domain audio data into effective energy points and ineffective energy points by comparing the amplitude of each energy point included in the time domain audio data with a conversion threshold; and calculating the statistic value of each energy point according to the statistic length so as to obtain the audio data subjected to statistic transformation.
7. A tower headroom audio monitoring system of a wind generating set, the system comprising:
the monitoring equipment is used for acquiring time domain audio data when the blades of the wind generating set sweep through the tower;
a processor configured to:
performing statistical transformation on the time domain audio data according to the periodically changed characteristics of the time domain audio data, wherein the periodically changed characteristics are obtained according to different distances between the blades and the tower when the blades rotate;
locating peak locations in the statistically transformed audio data to obtain peak amplitudes; and is
Comparing the peak amplitude to an amplitude threshold to determine whether to issue a tower headroom warning message;
wherein the processor is further configured to: classifying the time-domain audio data into valid energy points and invalid energy points by comparing an amplitude of each energy point included in the time-domain audio data with a conversion threshold; and calculating the statistic value of each energy point according to the statistic length so as to obtain the audio data subjected to statistic transformation.
8. The system of claim 7, wherein the monitoring device is an audio sensor disposed proximate the wind turbine generator set.
9. The system of claim 7, wherein the monitoring device is an audio sensor disposed on the wind turbine generator set.
10. A computer-readable storage medium storing a program, characterized in that the program comprises instructions for performing the monitoring method according to any one of claims 1-5.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911334440.XA CN113090471B (en) | 2019-12-23 | 2019-12-23 | Tower clearance audio monitoring system, method and device of wind generating set |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
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| CN201911334440.XA CN113090471B (en) | 2019-12-23 | 2019-12-23 | Tower clearance audio monitoring system, method and device of wind generating set |
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