CN115539324A - Wind turbine generator set fault diagnosis method and device and electronic equipment - Google Patents
Wind turbine generator set fault diagnosis method and device and electronic equipment Download PDFInfo
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- CN115539324A CN115539324A CN202211170472.2A CN202211170472A CN115539324A CN 115539324 A CN115539324 A CN 115539324A CN 202211170472 A CN202211170472 A CN 202211170472A CN 115539324 A CN115539324 A CN 115539324A
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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
- F03—MACHINES OR ENGINES FOR LIQUIDS; WIND, SPRING, OR WEIGHT MOTORS; PRODUCING MECHANICAL POWER OR A REACTIVE PROPULSIVE THRUST, NOT OTHERWISE PROVIDED FOR
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- F03D9/00—Adaptations of wind motors for special use; Combinations of wind motors with apparatus driven thereby; Wind motors specially adapted for installation in particular locations
- F03D9/20—Wind motors characterised by the driven apparatus
- F03D9/25—Wind motors characterised by the driven apparatus the apparatus being an electrical generator
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Abstract
The invention discloses a wind turbine generator fault diagnosis method, a wind turbine generator fault diagnosis device and electronic equipment, wherein the method comprises the following steps: acquiring various types of fault vibration data corresponding to the wind turbine generator; for any type of fault vibration data, acquiring a peak value of an impact signal in the fault vibration data and a position of the peak value in the fault vibration data; acquiring target impact signals of which the peak values are larger than a preset peak value threshold value, and extracting impact fragments of preset lengths from fault vibration data according to the positions of the peak values of all the target impact signals; carrying out frequency domain transformation on the impact segment corresponding to each extracted target impact signal to obtain a spectrogram of each impact segment; overlapping the spectrogram of each impact segment and determining a target frequency band energy range in the overlapped spectrogram; and establishing and obtaining wind turbine generator fault diagnosis comparison data according to each fault type in the wind turbine generator and the corresponding target frequency band energy range, and realizing accurate diagnosis of aperiodic impact faults.
Description
Technical Field
The invention relates to the technical field of wind turbine generator fault diagnosis, in particular to a wind turbine generator fault diagnosis method and device and electronic equipment.
Background
With the development of the wind power industry, the application of the fault diagnosis technology based on the vibration signal is more and more extensive, at present, vibration sensors and data acquisition equipment are installed on most parts of the wind turbine generator, and various types of faults of the wind turbine generator can be diagnosed and early warned through analyzing vibration data. At present, wind power vibration fault diagnosis can only carry out time domain analysis and frequency spectrum analysis on periodic signals or quasi-periodic signals, and for faults such as component cracks, cracking, abrasion collision and the like, the vibration signals of the wind power vibration fault diagnosis usually have irregular impact, and the conventional time domain and frequency spectrum analysis method cannot analyze the data. Therefore, a new wind turbine generator fault diagnosis method is urgently needed to be provided to ensure accurate diagnosis of faults for diagnosing aperiodic impact.
Disclosure of Invention
Therefore, the invention aims to overcome the defects that the conventional vibration signal has irregular impact on the faults such as component cracks and the like, and the conventional time domain and spectrum analysis cannot analyze the data, so as to provide a method, a device and electronic equipment for diagnosing the faults of the wind turbine generator.
According to a first aspect, an embodiment of the invention discloses a wind turbine generator fault diagnosis method, which comprises the following steps: acquiring various types of fault vibration data corresponding to the wind turbine generator; for any type of fault vibration data, acquiring a peak value of an impact signal in the fault vibration data and a position of the peak value in the fault vibration data; acquiring target impact signals with peak values larger than a preset peak value threshold value, and extracting impact fragments with preset lengths from fault vibration data according to the positions of the peak values of the target impact signals; carrying out frequency domain transformation on the impact segment corresponding to each extracted target impact signal to obtain a spectrogram of each impact segment; overlapping the spectrogram of each impact segment and determining a target frequency band energy range in the overlapped spectrogram; and establishing and obtaining wind turbine generator fault diagnosis comparison data according to each fault type in the wind turbine generator and the corresponding target frequency band energy range.
Optionally, the extracting an impact segment with a preset length from the fault vibration data according to the position of the peak of each target impact signal includes: taking the position of the peak value of each target impact signal as a center, and respectively extracting vibrator data of a target length from the left side and the right side; and taking the extracted vibration subdata as the impact fragment.
Optionally, the obtaining a peak value of an impact signal in the fault vibration data and a position of the peak value in the fault vibration data includes: dividing the fault vibration data into a plurality of equal parts and numbering each equal part in sequence; and determining the position of the peak value in the fault vibration data according to the length of each equal part, the number of the equal part where the peak value is located and the coordinate of the peak value in the equal part.
Optionally, before obtaining the peak value of the impact signal in the fault vibration data, the method includes: acquiring crest factors in the fault vibration data; comparing the crest factor with a preset crest factor threshold; and when the crest factor is greater than or equal to a preset crest factor threshold value, the impact signal exists in the fault vibration data.
Optionally, the method further comprises: training a preset machine learning model according to the fault diagnosis comparison data of the wind turbine generator set, so as to obtain a fault diagnosis model; when vibration data of a wind turbine generator set to be detected are obtained, processing the vibration data to obtain a corresponding target frequency band energy range; and inputting the target frequency band energy range into the fault diagnosis model to obtain the fault type of the wind turbine generator.
According to a second aspect, an embodiment of the present invention further discloses a wind turbine generator fault diagnosis apparatus, where the apparatus includes: the data acquisition module is used for acquiring various types of fault vibration data corresponding to the wind turbine generator; the peak position acquisition module is used for acquiring the peak value of an impact signal in fault vibration data and the position of the peak value in the fault vibration data for any type of fault vibration data; the impact fragment extraction module is used for acquiring target impact signals of which the peak values are larger than a preset peak value threshold value and extracting impact fragments of preset lengths from fault vibration data according to the positions of the peak values of the target impact signals; the frequency domain transformation module is used for carrying out frequency domain transformation on the impact segments corresponding to the extracted target impact signals to obtain a spectrogram of each impact segment; the energy range determining module is used for overlapping the spectrogram of each impact segment and determining a target frequency band energy range in the overlapped spectrogram; and the diagnostic data construction module is used for constructing and obtaining wind turbine generator fault diagnosis comparison data according to each fault type in the wind turbine generator and the corresponding target frequency band energy range.
Optionally, the peak position obtaining module includes: the numbering submodule is used for dividing the fault vibration data into a plurality of equal parts and numbering each equal part in sequence; and the position calculation submodule is used for determining the position of the peak value in the fault vibration data according to the length of each equal part, the number of the equal part where the peak value is located and the coordinates of the peak value in the equal part.
Optionally, the apparatus further comprises: the crest factor acquisition module is used for acquiring crest factors in the fault vibration data; the comparison module is used for comparing the crest factor with a preset crest factor threshold value; and the impact signal judging module is used for judging that the impact signal exists in the fault vibration data when the crest factor is greater than or equal to a preset crest factor threshold value.
According to a third aspect, an embodiment of the present invention further discloses an electronic device, including: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to cause the at least one processor to perform the steps of the wind turbine generator fault diagnosis method according to the first aspect or any one of the optional embodiments of the first aspect.
According to a fourth aspect, the embodiments of the present invention further disclose a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the steps of the wind turbine generator fault diagnosis method according to the first aspect or any optional embodiment of the first aspect.
The technical scheme of the invention has the following advantages:
according to the wind turbine generator fault diagnosis method provided by the invention, the peak value of an impact signal in fault diagnosis data and the position of the peak value in the fault vibration data are obtained, the impact fragment is extracted from the fault vibration data according to the position of the peak value of the impact signal to carry out frequency domain transformation to obtain a frequency spectrogram, a target frequency band energy range in the superposed frequency spectrogram is determined according to the frequency spectrogram, and wind turbine generator fault diagnosis comparison data are constructed according to any fault type and the corresponding target frequency band energy range. According to the fault diagnosis comparison data of the wind turbine generator, the periodic impact signals and the aperiodic signals can be analyzed, the energy range of the target frequency range of the impact segment in the vibration data can be extracted and analyzed from the data with the irregular impact signals, and compared with the fault diagnosis comparison data of the wind turbine generator, so that the accurate diagnosis of the aperiodic impact fault is realized.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
Fig. 1 is a flowchart of a specific example of a wind turbine generator fault diagnosis method according to an embodiment of the present invention;
fig. 2 is a schematic block diagram of a specific example of a wind turbine generator fault diagnosis device in the embodiment of the present invention;
fig. 3 is a diagram of a specific example of an electronic device in an embodiment of the invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the accompanying drawings, and it is to be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it should be noted that the terms "center", "upper", "lower", "left", "right", "vertical", "horizontal", "inner", "outer", etc., indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, and are only for convenience of description and simplicity of description, but do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be construed as limiting the present invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance.
In the description of the present invention, it should be noted that, unless otherwise explicitly specified or limited, the terms "mounted," "connected," and "connected" are to be construed broadly, e.g., as meaning either a fixed connection, a removable connection, or an integral connection; can be mechanically or electrically connected; the two elements may be directly connected or indirectly connected through an intermediate medium, or may be communicated with each other inside the two elements, or may be wirelessly connected or wired connected. The specific meanings of the above terms in the present invention can be understood in a specific case to those of ordinary skill in the art.
In addition, the technical features involved in the different embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
The embodiment of the invention discloses a wind turbine generator fault diagnosis method, which comprises the following steps of:
the method comprises the following steps of S1, acquiring various types of fault vibration data corresponding to the wind turbine generator; illustratively, the method comprises the steps of installing vibration sensors and data acquisition equipment on different parts of the wind turbine to acquire corresponding vibration data and obtain fault vibration data from the vibration data, or obtaining the fault vibration data from data stored with various types of historical fault vibration data of the wind turbine. The specific acquisition mode can be determined according to the actual situation.
S2, for any type of fault vibration data, acquiring a peak value of an impact signal in the fault vibration data and a position of the peak value in the fault vibration data;
for example, the embodiment of the present application does not limit the method for obtaining the location of the peak in the fault vibration data, and the embodiment of the present application calculates the location of the impact signal in the fault vibration data, finds the coordinate of the peak in the impact signal, and thereby calculates the location of the peak in the fault vibration data.
S3, acquiring target impact signals with peak values larger than a preset peak value threshold value, and extracting impact fragments with preset lengths from fault vibration data according to the positions of the peak values of the target impact signals;
exemplarily, a preset peak threshold is set in the embodiment of the present application (the preset peak threshold is an adjustable parameter, and may be determined by a person skilled in the art according to actual needs), and the preset peak threshold is 3*R in the embodiment of the present application; wherein, R is an effective value of the vibration data, and can be calculated by the following formula:
wherein N is the number of data points in the acquired fault vibration data, X i I =1,2, …, n, for the ith failure vibration data.
When the maximum peak value of each impact signal is greater than the preset peak value threshold, it indicates that the peak value needs to be extracted, in the embodiment of the present application, the position of the peak value obtained in step S2 in the fault vibration data is taken as the center, an impact segment with a preset length (the preset length is also an adjustable parameter) is extracted from the fault vibration data, and the impact segment may include segments with S vibration data points, and all the extracted impact segments are stored. By adjusting the preset peak value threshold of the impact signals, the number of the extracted target impact signals can be adjusted, so that the method is adjustable.
S4, performing frequency domain transformation on the impact segments corresponding to the extracted target impact signals to obtain a spectrogram of each impact segment; for example, in the embodiment of the present application, the frequency domain transformation is performed on the extracted impact segment, but not limited to, a windowed fourier transform is used to obtain a spectrogram, the selection of different window functions is determined according to actual situations, and a hanning window is used for fourier transform in the embodiment of the present application.
S5, overlapping the spectrogram of each impact segment and determining a target frequency band energy range in the overlapped spectrogram;
for example, in the embodiment of the present application, frequency domain transforms are sequentially performed on each extracted impulse segment, a spectrogram is superimposed, a self-power spectrum analysis is performed according to the superimposed spectrogram (a method for analyzing the spectrogram is only used as an example, but not limited thereto), frequency band energy ranges of a plurality of impulse signal segments are determined, and a target frequency band energy range is obtained from the frequency band energy ranges, where the target frequency band is a frequency band in which an energy value in the target frequency band range is greater than a preset energy value threshold, and the preset energy threshold is determined according to an actual requirement.
And S6, establishing and obtaining wind turbine generator fault diagnosis comparison data according to each fault type in the wind turbine generator and the corresponding target frequency band energy range.
For example, the steps are repeated for each type of known fault, the target frequency band energy range of each fault type is determined, and then a diagnosis comparison data list of each fault type is constructed, so that the fault of the wind turbine generator can be diagnosed according to the normal data and the diagnosis comparison data list of each fault type.
The method for diagnosing the faults of the wind turbine generator comprises the steps of calculating the position of a peak value of an impact signal in fault vibration data in the fault vibration data, extracting impact fragments with preset lengths from the fault vibration data according to the position of the peak value larger than a preset peak value threshold value, carrying out frequency domain transformation on each extracted impact fragment to obtain a frequency spectrogram of the impact fragment, superposing the frequency spectrogram of each impact fragment, determining a target frequency band energy range in the superposed frequency spectrogram, constructing and obtaining fault diagnosis comparison data of the wind turbine generator according to each fault type of the wind turbine generator and the corresponding target frequency band energy range, analyzing periodic impact signals and aperiodic signals through the constructed fault diagnosis comparison data of the wind turbine generator, comparing the energy range of the target frequency band of the impact fragment in the analyzed vibration data with the fault diagnosis comparison data of the wind turbine generator, and accurately diagnosing aperiodic impact faults.
As an optional embodiment of the present invention, step S3 includes: taking the position of the peak value of each target impact signal as a center, and respectively extracting vibrator data of a target length from the left side and the right side; and extracting vibration subdata as an impact fragment.
For example, the embodiment of the present application does not limit a manner of intercepting the vibrator data of the target length, the extracted vibrator data includes not only the impact signal but also a periodic signal or a quasi-periodic signal, and the extracted vibrator data corresponding to the left and right sides are combined to obtain the impact segments. The target length is not limited in the embodiments of the present application, and can be determined by those skilled in the art according to actual needs. In the embodiment of the application, vibration subdata with 0.55 fault vibration data points on the left side and the right side is selected.
As an optional embodiment of the present invention, the step S2 of obtaining a peak value of the impact signal in the fault vibration data and a position of the peak value in the fault vibration data includes: dividing fault vibration data into a plurality of equal parts and numbering each equal part in sequence; and determining the position of the peak value in the fault vibration data according to the length of each equal part, the number of the equal part where the peak value is located and the coordinate of the peak value in the equal part. In the embodiment of the application, the fault vibration data is divided into a plurality of equal parts to calculate the position of the peak value in the fault vibration data, so that the preset peak value threshold value is not required to be higher, the impact fragments with smaller peak values are avoided being missed, and the number of the impact fragments can be controlled and extracted by adjusting the number of the divided equal parts.
Illustratively, the embodiment of the application divides the fault vibration data into a plurality of equal parts, obtains the coordinate of a peak value in the equal part as m, obtains the coordinate length of each equal part as S, calculates the sequence of the equal parts in the fault vibration data as k, and obtains the coordinate of the peak value in the fault vibration data as m + (k-1) × S.
As an optional embodiment of the present invention, before obtaining a peak value of an impact signal in fault vibration data, the method includes: acquiring crest factors in fault vibration data; comparing the crest factor with a preset crest factor threshold; and when the crest factor is greater than or equal to the preset crest factor threshold value, an impact signal exists in the fault vibration data.
For example, the crest factor in the fault vibration data may be calculated by:
C＝Pk/R
where C is a crest factor, pk = MAX (D), representing the maximum peak in the fault vibration data, D representing the peak in the fault vibration data, and R being the effective value of the vibration data.
The crest factor threshold value is determined according to the actual situation, the crest factor threshold value set in the embodiment of the application can be set to be 3, the calculated crest factor of the fault vibration data is compared with the set crest factor threshold value, if the calculated crest factor is larger than or equal to the set crest factor threshold value, an impact signal exists in the fault vibration data, and if the calculated crest factor is smaller than the set crest factor threshold value, the impact signal does not exist in the vibration data.
As an optional embodiment of the present invention, the method further comprises: training a preset machine learning model according to the fault diagnosis comparison data of the wind turbine generator set, so as to obtain a fault diagnosis model; when vibration data of a wind turbine generator set to be detected are obtained, processing the vibration data to obtain a corresponding target frequency band energy range; and inputting the target frequency band energy range into the fault diagnosis model to obtain the fault type of the wind turbine generator, and training a preset machine learning model according to fault comparison data, so that the intelligence and the accuracy of the fault diagnosis of the wind turbine generator are improved, and the labor cost is reduced.
According to the wind turbine generator set fault diagnosis method provided by the embodiment of the invention, the extracted impact signals are processed, corresponding frequency spectrum and envelope analysis is carried out, and the priori knowledge base is established according to the target frequency band energy ranges corresponding to different faults so as to early warn different types of generator set faults.
The embodiment of the invention also discloses a wind turbine generator fault diagnosis device, as shown in fig. 2, the device comprises: the data acquisition module 101 is used for acquiring various types of fault vibration data corresponding to the wind turbine generator; the peak position acquisition module 102 is configured to acquire, for any type of fault vibration data, a peak value of an impact signal in the fault vibration data and a position of the peak value in the fault vibration data; the impact segment extraction module 103 is configured to acquire target impact signals with peak values larger than a preset peak value threshold and extract an impact segment with a preset length from the fault vibration data according to a position of a peak value of each target impact signal; the frequency domain transformation module 104 is configured to perform frequency domain transformation on the impact segments corresponding to the extracted target impact signals to obtain a spectrogram of each impact segment; an energy range determining module 105, configured to superimpose the spectrogram of each impact segment and determine a target frequency band energy range in the superimposed spectrogram; and the diagnostic data construction module 106 is used for constructing and obtaining wind turbine generator fault diagnosis comparison data according to each fault type in the wind turbine generator and the corresponding target frequency band energy range.
The wind turbine generator fault diagnosis device provided by the invention is used for acquiring various types of fault diagnosis data corresponding to a wind turbine generator, acquiring the peak value and the position of the peak value of an impact signal of any type of fault vibration data, extracting an impact segment with a preset length from the fault vibration data by taking the peak value position larger than a preset peak value threshold value as the center, carrying out frequency domain transformation on the extracted impact segment to obtain a frequency spectrogram of each impact segment, superposing the frequency spectrograms of each impact segment to determine a target frequency band energy range, obtaining wind turbine generator fault diagnosis comparison data according to each fault type and the corresponding target frequency band energy range, and not only analyzing periodic impact signals but also analyzing aperiodic signals through the constructed wind turbine generator fault diagnosis comparison data, and comparing the energy range of the target frequency band of the impact segment in the analyzed vibration data with the wind turbine generator fault diagnosis comparison data to realize accurate diagnosis of aperiodic impact faults.
As an optional embodiment of the present invention, the impact segment extracting module 103 is further configured to extract vibrator data of a target length from left and right sides respectively by taking a position where a peak of each target impact signal is located as a center, and use the extracted vibrator data as the impact segment.
As an optional implementation manner of the present invention, the peak position obtaining module 102 further includes: the numbering submodule is used for dividing the fault diagnosis data into a plurality of equal parts and numbering each equal part in sequence; and the position calculation submodule is used for calculating and determining the position of the peak value in the fault vibration data according to the length of each equal part, the number of the equal part where the peak value is located and the coordinates of the peak value in the equal part.
As an optional embodiment of the present invention, the apparatus further comprises: the crest factor acquisition module is used for acquiring crest factors in the fault vibration data; the comparison module is used for comparing the crest factor with a preset crest factor threshold value; and the impact signal judging module is used for judging that the fault vibration data has the impact signal when the crest factor is greater than or equal to a preset crest factor threshold value.
As an optional embodiment of the present invention, the apparatus further comprises: the model training module is used for training a preset machine learning model according to the fault diagnosis comparison data of the wind turbine generator set, so as to obtain a fault diagnosis model; the data processing module is used for processing vibration data to obtain a corresponding target frequency band energy range when the vibration data of the wind turbine generator to be detected are obtained; and the fault diagnosis module is used for inputting the target frequency band energy range into the fault diagnosis model to obtain the fault type of the wind turbine generator.
An embodiment of the present invention further provides an electronic device, as shown in fig. 3, the electronic device may include a processor 401 and a memory 402, where the processor 401 and the memory 402 may be connected by a bus or in another manner, and fig. 3 takes the connection by the bus as an example.
The memory 402, which is a non-transitory computer readable storage medium, may be used to store non-transitory software programs, non-transitory computer executable programs, and modules, such as program instructions/modules corresponding to the wind turbine generator fault diagnosis method in the embodiment of the present invention. The processor 401 executes various functional applications and data processing of the processor by running the non-transitory software programs, instructions and modules stored in the memory 402, that is, the wind turbine generator fault diagnosis method in the above method embodiment is implemented.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created by the processor 401, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, memory 402 may optionally include memory located remotely from processor 401, which may be connected to processor 401 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more modules are stored in the memory 402, and when executed by the processor 401, perform the wind turbine fault diagnosis method in the embodiment shown in fig. 1.
The details of the electronic device may be understood with reference to the corresponding related description and effects in the embodiment shown in fig. 1, and are not described herein again.
It will be understood by those skilled in the art that all or part of the processes of the methods of the embodiments described above can be implemented by a computer program, which can be stored in a computer-readable storage medium, and when executed, can include the processes of the embodiments of the methods described above. The storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a Flash Memory (Flash Memory), a Hard Disk (Hard Disk Drive, abbreviated as HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope defined.
Claims (10)
1. A wind turbine generator system fault diagnosis method is characterized by comprising the following steps:
acquiring various types of fault vibration data corresponding to the wind turbine generator;
for any type of fault vibration data, acquiring a peak value of an impact signal in the fault vibration data and a position of the peak value in the fault vibration data;
acquiring target impact signals with peak values larger than a preset peak value threshold value, and extracting impact fragments with preset lengths from fault vibration data according to the positions of the peak values of the target impact signals;
carrying out frequency domain transformation on the impact segment corresponding to each extracted target impact signal to obtain a spectrogram of each impact segment;
overlapping the spectrogram of each impact segment and determining a target frequency band energy range in the overlapped spectrogram;
and establishing and obtaining wind turbine generator fault diagnosis comparison data according to each fault type in the wind turbine generator and the corresponding target frequency band energy range.
2. The wind turbine generator system fault diagnosis method according to claim 1, wherein the extracting of the impact segment with the preset length from the fault vibration data according to the position of the peak value of each target impact signal comprises:
taking the position of the peak value of each target impact signal as a center, and respectively extracting vibrator data of a target length from the left side and the right side;
and taking the extracted vibration subdata as the impact fragment.
3. The wind turbine generator system fault diagnosis method according to claim 1, wherein the obtaining of the peak value of the impact signal in the fault vibration data and the position of the peak value in the fault vibration data comprises:
dividing the fault vibration data into a plurality of equal parts and numbering each equal part in sequence;
and determining the position of the peak value in the fault vibration data according to the length of each equal part, the number of the equal part where the peak value is located and the coordinates of the peak value in the equal parts.
4. The wind turbine generator system fault diagnosis method according to claim 1, wherein before the obtaining of the peak value of the impact signal in the fault vibration data, the method comprises:
acquiring crest factors in the fault vibration data;
comparing the crest factor with a preset crest factor threshold;
and when the crest factor is greater than or equal to a preset crest factor threshold value, the impact signal exists in the fault vibration data.
5. The wind turbine generator system fault diagnosis method according to claim 1, characterized in that the method further comprises:
training a preset machine learning model according to the fault diagnosis comparison data of the wind turbine generator set, so as to obtain a fault diagnosis model;
when vibration data of a wind turbine generator set to be detected are obtained, processing the vibration data to obtain a corresponding target frequency band energy range;
and inputting the target frequency band energy range into the fault diagnosis model to obtain the fault type of the wind turbine generator.
6. A wind turbine generator system fault diagnosis apparatus, characterized in that the apparatus comprises:
the data acquisition module is used for acquiring various types of fault vibration data corresponding to the wind turbine generator;
the peak position acquisition module is used for acquiring the peak value of an impact signal in fault vibration data and the position of the peak value in the fault vibration data for any type of fault vibration data;
the impact segment extraction module is used for acquiring target impact signals of which the peak values are larger than a preset peak value threshold value and extracting impact segments of preset length from the fault vibration data according to the positions of the peak values of the target impact signals;
the frequency domain transformation module is used for carrying out frequency domain transformation on the impact segments corresponding to the extracted target impact signals to obtain a spectrogram of each impact segment;
the energy range determining module is used for overlapping the spectrogram of each impact segment and determining a target frequency band energy range in the overlapped spectrogram;
and the diagnostic data construction module is used for constructing and obtaining wind turbine generator fault diagnosis comparison data according to each fault type in the wind turbine generator and the corresponding target frequency band energy range.
7. The wind turbine generator system fault diagnosis device according to claim 6, wherein the peak position obtaining module includes:
the numbering submodule is used for dividing the fault vibration data into a plurality of equal parts and numbering each equal part in sequence;
and the position calculation submodule is used for determining the position of the peak value in the fault vibration data according to the length of each equal part, the number of the equal part where the peak value is located and the coordinates of the peak value in the equal part.
8. The wind turbine generator system fault diagnosis device according to claim 6, characterized in that the device further comprises:
the crest factor acquisition module is used for acquiring crest factors in the fault vibration data;
the comparison module is used for comparing the crest factor with a preset crest factor threshold value;
and the impact signal judging module is used for judging that the impact signal exists in the fault vibration data when the crest factor is greater than or equal to a preset crest factor threshold value.
9. An electronic device, comprising: at least one processor; and a memory communicatively coupled to the at least one processor; wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the steps of the wind turbine generator system fault diagnosis method according to any one of claims 1-5.
10. A computer-readable storage medium, on which a computer program is stored, which, when being executed by a processor, carries out the steps of the method for diagnosing a fault in a wind turbine according to any one of claims 1 to 5.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116484267A (en) * | 2023-06-20 | 2023-07-25 | 南方电网科学研究院有限责任公司 | Transformer fault characteristic extraction and determination method, computer equipment and storage medium |
| CN116861219A (en) * | 2023-09-01 | 2023-10-10 | 华能新能源股份有限公司山西分公司 | Wind turbine generator pitch-variable fault diagnosis method |
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2022
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116484267A (en) * | 2023-06-20 | 2023-07-25 | 南方电网科学研究院有限责任公司 | Transformer fault characteristic extraction and determination method, computer equipment and storage medium |
| CN116484267B (en) * | 2023-06-20 | 2023-09-19 | 南方电网科学研究院有限责任公司 | Transformer fault characteristic extraction and determination method, computer equipment and storage medium |
| CN116861219A (en) * | 2023-09-01 | 2023-10-10 | 华能新能源股份有限公司山西分公司 | Wind turbine generator pitch-variable fault diagnosis method |
| CN116861219B (en) * | 2023-09-01 | 2023-12-15 | 华能新能源股份有限公司山西分公司 | Wind turbine generator pitch-variable fault diagnosis method |
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