CN113985276A - Fault diagnosis method and device of wind generating set - Google Patents
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Abstract
The application provides a fault diagnosis method and a fault diagnosis device for a wind generating set, wherein the method comprises the following steps: acquiring a current signal and vibration data of the wind generating set; determining the angular displacement of a generator of the wind generating set according to the current signal; determining a first order spectrum of the wind generating set according to the angular displacement and the vibration data, wherein a first coordinate axis of the first order spectrum is used for representing the angular displacement, a second coordinate axis of the first order spectrum is used for representing the vibration data, and the angular displacement comprises a plurality of unit angular displacements; performing interpolation processing on the first order spectrum to enable the quantity of vibration data corresponding to each unit angular displacement to meet a preset condition, and obtaining a second order spectrum; and analyzing the second-order spectrum by adopting a fault diagnosis algorithm. The non-rotation-speed order tracking mode is more accurate and stable, the system cost is reduced, and the accuracy and the definition of fault diagnosis are improved through interpolation processing.
Description
Technical Field
The application relates to the field of fault diagnosis, in particular to a fault diagnosis method and device for a wind generating set.
Background
At present, the fault of the wind generating set can be predicted according to the collected rotating speed of the wind generating set, and the loss caused by the shutdown of the wind generating set is avoided. For a wind generating set, the bearing vibration and generator fault diagnosis technology under a constant speed is relatively mature, and basic FFT analysis, envelope analysis, wavelet analysis, spectrum analysis and the like can meet basic analysis requirements. However, in actual circumstances, the generator is often in a state of variable rotational speed. Generally, the acquisition of the rotating speed is mainly completed through a rotating speed sensor, and the synchronous acquisition of vibration data and the rotating speed is performed.
Disclosure of Invention
The application provides a fault diagnosis method and device for a wind generating set.
Specifically, the method is realized through the following technical scheme:
a first aspect of an embodiment of the present application provides a method for diagnosing a fault of a wind turbine generator system, including:
acquiring a current signal and vibration data of the wind generating set;
determining the angular displacement of a generator of the wind generating set according to the current signal;
determining a first order spectrum of the wind generating set according to the angular displacement and the vibration data, wherein a first coordinate axis of the first order spectrum is used for representing the angular displacement, a second coordinate axis of the first order spectrum is used for representing the vibration data, and the angular displacement comprises a plurality of unit angular displacements;
performing interpolation processing on the first order spectrum to enable the quantity of vibration data corresponding to each unit angular displacement to meet a preset condition, and obtaining a second order spectrum;
and analyzing the second order spectrum by adopting a fault diagnosis algorithm.
Optionally, the number of the vibration data corresponding to each unit angular displacement satisfies a preset condition, and includes:
the number of the vibration data corresponding to each unit angular displacement reaches the maximum number of the vibration data corresponding to a plurality of unit angular displacements; alternatively, the first and second electrodes may be,
the number of the vibration data corresponding to each unit angular displacement reaches a preset number threshold.
Optionally, the interpolating the first order spectrum to make the number of vibration data corresponding to each unit angular displacement satisfy a preset condition, and obtaining a second order spectrum includes:
and performing interpolation processing on the first order spectrum by adopting a multi-style interpolation algorithm, so that the quantity of vibration data corresponding to each unit angular displacement meets a preset condition, and obtaining a second order spectrum.
Optionally, said determining an angular displacement of a generator of said wind park from said current signal comprises:
determining a rotational angular velocity of the generator from the current signal;
determining an angular displacement of the generator based on the angular rotation velocity.
Optionally, said determining a rotational angular speed of said generator from said current signal comprises:
reconstructing the current signal by adopting a synchronous compression wavelet technology, and extracting a rotating speed component of the generator;
determining a rotational angular speed of the generator based on the rotational speed component.
Optionally, after the current signal is reconstructed by using the synchronous compression wavelet technique, and after the rotational speed component of the generator is extracted, before the rotational angular speed of the generator is determined according to the rotational speed component, the method further includes:
determining the ratio of the rotating speed component in a preset time period to a preset rotating speed interval;
the determining a rotation angular velocity of the generator based on the rotation speed component includes:
and when the occupation ratio is larger than or equal to a preset occupation ratio threshold value, determining the rotation angular speed of the generator according to the rotation speed component.
Optionally, the angular displacement is characterized by a number of turns of the generator;
and/or the current signal is obtained based on current probe detection and/or the current signal is determined based on the output power of the wind generating set;
and/or the vibration data is the vibration data of the driving end of the generator obtained based on the detection of a vibration sensor;
and/or the fault diagnosis algorithm comprises fast Fourier transform and/or envelope analysis and/or wavelet analysis.
Optionally, the wind turbine generator system is a semi-direct drive wind turbine generator system.
A second aspect of embodiments of the present application provides a fault diagnosis device for a wind turbine generator system, including one or more processors, configured to implement the fault diagnosis method according to the first aspect.
A third aspect of embodiments of the present application provides a computer-readable storage medium on which a program is stored, which, when executed by a processor, implements the fault diagnosis method described in the first aspect.
According to the technical scheme provided by the embodiment of the application, the angular displacement of the generator is determined according to the current signal of the wind generating set, compared with a mode of determining the angular displacement of the generator by adopting a rotating speed signal of the generator, the non-rotating-speed order tracking mode of the application is more accurate and stable, the system cost is reduced, and engineering and practice optimization is realized; and interpolation processing is carried out on the first order spectrum, the quantity of vibration data corresponding to each unit angular displacement is ensured, and fault diagnosis analysis is carried out on the second order spectrum obtained by interpolation processing, so that the precision and the definition of fault diagnosis are improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic flow diagram illustrating a method for fault diagnosis of a wind turbine generator system according to an exemplary embodiment of the present disclosure;
FIG. 2 is a graphical illustration of a current signal of a wind turbine generator set according to an exemplary embodiment of the present application;
FIG. 3 is a schematic diagram illustrating a number of revolutions of a generator of a wind turbine generator system in relation to a rotational speed component according to an exemplary embodiment of the present application;
FIG. 4A is a graphical illustration of a first order spectrum shown in an exemplary embodiment of the present application;
FIG. 4B is a graphical illustration of a second order spectrum shown in an exemplary embodiment of the present application;
FIG. 5A is a schematic diagram of a spectrum obtained after Fast Fourier Transform (FFT) analysis of a first order spectrum according to an exemplary embodiment of the present application;
FIG. 5B is a schematic diagram of a spectrum obtained after Fast Fourier Transform (FFT) analysis of a second order spectrum according to an exemplary embodiment of the present application;
fig. 6 is a schematic structural diagram of a fault diagnosis device of a wind turbine generator set according to an exemplary embodiment of the present application.
Detailed Description
Reference will now be made in detail to the exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, like numbers in different drawings represent the same or similar elements unless otherwise indicated. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with certain aspects of the present application, as detailed in the appended claims.
The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used in this application and the appended claims, the singular forms "a", "an", and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any and all possible combinations of one or more of the associated listed items.
It is to be understood that although the terms first, second, third, etc. may be used herein to describe various information, such information should not be limited to these terms. These terms are only used to distinguish one type of information from another. For example, first information may also be referred to as second information, and similarly, second information may also be referred to as first information, without departing from the scope of the present application. The word "if" as used herein may be interpreted as "at … …" or "when … …" or "in response to a determination", depending on the context.
The method and the device for diagnosing the fault of the wind generating set are described in detail below with reference to the accompanying drawings. The features of the following examples and embodiments may be combined with each other without conflict.
Compared with the mode of determining the angular displacement of the generator by adopting the rotating speed signal of the generator, the non-rotating-speed order tracking mode is more accurate and stable, the system cost is reduced, and engineering and practice optimization is realized; and interpolation processing is carried out on the first order spectrum, the quantity of vibration data corresponding to each unit angular displacement is ensured, and fault diagnosis analysis is carried out on the second order spectrum obtained by interpolation processing, so that the precision and the definition of fault diagnosis are improved.
Fig. 1 is a schematic flow chart of a fault diagnosis method for a wind turbine generator system according to an exemplary embodiment of the present application. As shown in fig. 1, the fault diagnosis method in an embodiment of the present application may include steps S11 to S15.
In step S11, a current signal and vibration data of the wind turbine generator system are acquired.
In the embodiment of the application, the current signal and the vibration data are synchronously acquired.
The current signal may be obtained in different ways, for example, in some embodiments, the current signal is obtained based on a current probe detection, for example, the current probe may be provided at an output of a generator of the wind turbine generator system, or the current probe may be provided at an input of the generator. It should be understood that the current probe may be replaced with other sensors capable of detecting a current signal.
In other embodiments, the current signal is determined based on the output power of the wind turbine generator system, for example, data of the grid side may be obtained and analyzed to obtain the magnitude of the output power of the wind turbine generator system.
The vibration data is vibration data of the driving end of the generator obtained based on detection of the vibration sensor, wherein the vibration data comprises vibration amplitude and vibration frequency.
In S12, an angular displacement of a generator of the wind turbine is determined based on the current signal.
Specifically, determining the angular displacement of the generator of the wind turbine generator set according to the current signal in step S12 may include, but is not limited to, the following steps:
(1) determining the rotation angular speed of the generator according to the current signal;
firstly, a synchronous compression wavelet technology can be adopted to reconstruct a current signal and extract a rotating speed component of the generator; and determining the rotation angular speed of the generator according to the rotation speed component. Wherein the current signal is positively correlated, but not linearly positively correlated, with the rotational speed component of the generator. The method for extracting the rotating speed component by the synchronous compression wavelet technology is accurate in estimation, and is beneficial to improving the accuracy of subsequent fault diagnosis. It should be understood that other algorithms may be employed to extract the rotational speed component of the generator from the current signal.
In addition, in some embodiments, it is necessary to verify the reliability of the estimation of the rotation speed component, and the present embodiment determines the occupation ratio of the rotation speed component in a preset time period (which may be a sampling time period) in a preset rotation speed interval before determining the rotation angular speed of the generator according to the rotation speed component after the current signal is reconstructed by using a synchronous compression wavelet technology to extract the rotation speed component of the generator. And when the occupation ratio is greater than or equal to a preset occupation ratio threshold value, determining the rotation angular speed of the generator according to the rotation speed component. The occupation ratio is larger than or equal to the preset occupation ratio threshold value, so that the estimation of the rotating speed component is reliable, and the rotating angular speed of the generator can be further determined according to the rotating speed component. When the ratio is smaller than the preset ratio threshold value, the rotation speed estimation is unreliable, and at the moment, other algorithms can be adopted to estimate the rotation component again, or the sampling amount of the current signal is increased to estimate the rotation component again, and the like. The size of the preset duty ratio threshold may be set as desired, for example, the preset duty ratio threshold may be 85%, 90%, 95%, or 100%. The rotating speed interval can be obtained after up-and-down floating according to the rated rotating speed of the wind generating set, for example, the rated rotating speed is 1800rpm/min, and the rotating speed difference can be [1650,1850] rpm/min.
It should be understood that the reliability of the estimation of the rotation speed component may also be verified in other manners, for example, after the current signal is reconstructed by using the synchronous compression wavelet technique to extract the rotation speed component of the generator, before the rotation angular speed of the generator is determined according to the rotation speed component, the number of the rotation speed components within the preset time period within the preset rotation speed interval is determined, and when the number of the rotation speed components within the preset rotation speed interval is greater than or equal to the preset number threshold, the estimation of the rotation speed component is reliable, and the rotation angular speed of the generator may be further determined according to the rotation speed component; and when the number of the rotating speed components in the preset time period in the preset rotating speed interval is smaller than the preset number threshold, the estimation of the rotating speed components is unreliable.
The rotational angular velocity may be characterized by a curve form, such as the horizontal axis representing time and the vertical axis representing rotational angular velocity; alternatively, the angular rotation speed may be characterized by a discrete numerical form.
(2) And determining the angular displacement of the generator according to the rotation angular velocity.
The angular displacement of the generator can be obtained by performing division operation on the operation time curve and the rotation angular velocity curve of the wind generating set.
In some embodiments, the angular displacement is characterized by the number of turns of the generator; in yet other embodiments, the angular displacement may be characterized by the magnitude of the total angular rotation of the generator.
In S13, according to the angular displacement and the vibration data, a first order spectrum of the wind generating set is determined, a first coordinate axis of the first order spectrum is used for representing the angular displacement, a second coordinate axis of the first order spectrum is used for representing the vibration data, and the angular displacement comprises a plurality of unit angular displacements.
Optionally, in some embodiments, the first coordinate axis is a horizontal axis and the second coordinate axis is a vertical axis; in some other embodiments, the first axis is a vertical axis and the second axis is a horizontal axis.
Illustratively, the horizontal axis of the first order spectrum is used to characterize the number of turns of the generator and the vertical axis is used to characterize the vibration amplitude.
In the embodiment of the application, the vibration data is divided according to the angular displacement to obtain a first order spectrum, specifically, a sampling time period corresponding to one turn of rotation of the generator is obtained, and the vibration data obtained by sampling in the sampling time period corresponds to the corresponding turn number.
The unit angular displacement may be a number of turns or an angle corresponding to one turn of the generator, and it should be understood that the magnitude of the unit angular displacement may be set as desired.
In S14, the first order spectrum is interpolated so that the number of vibration data corresponding to each unit angular displacement satisfies a preset condition, and a second order spectrum is obtained.
In some embodiments, the number of vibration data corresponding to each unit angular displacement satisfying the preset condition includes: the number of vibration data corresponding to each unit angular displacement reaches the maximum number among the number of vibration data corresponding to a plurality of unit angular displacements. For example, the number of the vibration data corresponding to each unit angular displacement may be counted first, then the maximum number of the vibration data corresponding to all the unit angular displacements is determined, and then the vibration data in other unit angular displacements are interpolated according to the maximum number, so that the number of the vibration data corresponding to other unit angular displacements reaches the maximum number. After interpolation is carried out, the vibration data corresponding to a plurality of unit angular displacements are consistent in quantity and enough in quantity, and the accuracy of fault diagnosis can be improved.
In some other embodiments, the number of vibration data per unit angular displacement satisfying the preset condition includes: the number of the vibration data corresponding to each unit angular displacement reaches a preset number threshold. In this embodiment, after the interpolation is performed, the number of the vibration data corresponding to a plurality of unit angular displacements may be inconsistent, but the number is sufficient, so that the accuracy of fault diagnosis can be ensured.
Interpolation processing can be performed on the first order spectrum by adopting a multi-style interpolation algorithm, so that the quantity of vibration data corresponding to each unit angular displacement meets a preset condition, and a second order spectrum is obtained. It should be understood that other interpolation algorithms may be used to interpolate the first order spectrum, so that the number of vibration data corresponding to each unit angular displacement satisfies the preset condition, and the second order spectrum is obtained.
For example, when step S14 is implemented, the number of vibration data corresponding to each unit angular displacement is counted, then the maximum number of the number of vibration data corresponding to all unit angular displacements is determined, and then the vibration data in other unit angular displacements are interpolated by using a multi-pattern interpolation algorithm, so that the number of vibration data corresponding to other unit angular displacements reaches the maximum number.
At S15, the second order spectrum is analyzed using a fault diagnosis algorithm.
The fault diagnosis algorithm may include fast fourier transform and/or envelope analysis and/or wavelet analysis, etc.
The wind generating set in the embodiment of the application can be a semi-direct-drive wind generating set, namely, the fault diagnosis method in the embodiment is suitable for the semi-direct-drive wind generating set; it should be understood that the fault diagnosis method in the above embodiments is also applicable to other types of wind turbine generators.
In a specific embodiment, as shown in fig. 2, a graph of the current signal of the generator sampled by the current probe is shown, wherein the horizontal axis represents the sampling time t, and the vertical axis represents the current signal v obtained after the sampled voltage amplitude is normalized. The current signal shown in fig. 2 is processed based on the synchronous compression wavelet technique, and the rotational speed component is predicted, so as to obtain a relation curve between the number of turns of the generator rotation and the rotational speed component as shown in fig. 3, wherein in fig. 3, the horizontal axis represents the number of turns of the generator rotation, and the vertical axis represents the rotational speed component rpm. The rated rotating speed of the half-direct-drive wind generating set is 1800rpm/min, the number of the predicted rotating speed components is 1080, 95% of the predicted rotating speed components are distributed in a rotating speed interval of 1650-1850, and the estimation of the rotating speed components is reliable. Fig. 4A is a graph illustrating a first order spectrum determined according to the number of rotations of the generator and vibration data, and fig. 4B is a graph illustrating a second order spectrum obtained by multi-pattern interpolation of the first order spectrum shown in fig. 4A, where in fig. 4A and 4B, a horizontal axis represents the number of rotations and a vertical axis represents the vibration amplitude a, and it can be seen that the data amount of the second order spectrum is denser than that of the first order spectrum. At this time, the first order spectrum and the second order spectrum are both time domain curves. In order to perform fault diagnosis, the first order spectrum or the second order spectrum in the time domain needs to be further converted into a frequency domain curve, as shown in fig. 5A, the frequency spectrum obtained after performing fast fourier transform FFT analysis on the first order spectrum, as shown in fig. 5B, the frequency spectrum obtained after performing fast fourier transform FFT analysis on the second order spectrum, and from the comparison result, the frequency spectrum characteristic after no-rotation-speed-order tracking shown in fig. 5B is clearer than the frequency spectrum characteristic after no-rotation-speed-order tracking shown in fig. 5A, and sidebands are reduced. In addition, sampling rotating speed data is omitted in the processing process and is directly applied, and the fault diagnosis method is simpler, strong in stability and high in accuracy. In fig. 5A and 5B, the horizontal axis represents frequency, and the vertical axis represents vibration amplitude. The vibration amplitude is larger than or equal to a preset amplitude threshold value, and the fact that the wind generating set has faults at the corresponding sampling moment is indicated.
The acquisition of the current signal is relatively easy, the rotation frequency of the generator and the pulsation frequency of the current signal have a strong correlation, and the current signal is far more stable than vibration data although the rotation speed is changed, so that the fault diagnosis precision of the wind generating set based on the current signal can be greatly improved.
Corresponding to the embodiment of the fault diagnosis method, the application also provides an embodiment of the fault diagnosis device.
Referring to fig. 6, an embodiment of the present application further provides a fault diagnosis device for a wind turbine generator system, which includes one or more processors, and is configured to implement the fault diagnosis method in the foregoing embodiment.
The embodiment of the fault diagnosis device can be applied to the wind generating set. The device embodiments may be implemented by software, or by hardware, or by a combination of hardware and software. The software implementation is taken as an example, and as a device in a logical sense, a processor of the wind turbine generator set where the device is located reads corresponding computer program instructions in the nonvolatile memory into the memory for operation. From a hardware aspect, as shown in fig. 6, the hardware structure diagram of the wind turbine generator system where the fault diagnosis device is located in the present application is shown, except for the processor, the memory, the network interface, and the nonvolatile memory shown in fig. 6, the wind turbine generator system where the device is located in the embodiment may also include other hardware according to the actual function of the wind turbine generator system, which is not described again.
The implementation process of the functions and actions of each unit in the above device is specifically described in the implementation process of the corresponding step in the above method, and is not described herein again.
For the device embodiments, since they substantially correspond to the method embodiments, reference may be made to the partial description of the method embodiments for relevant points. The above-described embodiments of the apparatus are merely illustrative, and the units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the scheme of the application. One of ordinary skill in the art can understand and implement it without inventive effort.
An embodiment of the present application also provides a computer-readable storage medium, on which a program is stored, and when the program is executed by a processor, the fault diagnosis method in the above-described embodiment is implemented.
The computer readable storage medium may be an internal storage unit, such as a hard disk or a memory, of the wind turbine generator system according to any of the foregoing embodiments. The computer readable storage medium may also be an external storage device of the wind turbine, such as a plug-in hard disk, a Smart Media Card (SMC), an SD Card, a Flash memory Card (Flash Card), and the like, provided on the device. Further, the computer readable storage medium may also comprise both an internal storage unit of the wind park and an external storage device. The computer-readable storage medium is used for storing the computer program and other programs and data required by the wind park and may also be used for temporarily storing data that has been or will be output.
The above description is only exemplary of the present application and should not be taken as limiting the present application, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the scope of protection of the present application.
Claims (10)
1. A fault diagnosis method of a wind generating set is characterized by comprising the following steps:
acquiring a current signal and vibration data of the wind generating set;
determining the angular displacement of a generator of the wind generating set according to the current signal;
determining a first order spectrum of the wind generating set according to the angular displacement and the vibration data, wherein a first coordinate axis of the first order spectrum is used for representing the angular displacement, a second coordinate axis of the first order spectrum is used for representing the vibration data, and the angular displacement comprises a plurality of unit angular displacements;
performing interpolation processing on the first order spectrum to enable the quantity of vibration data corresponding to each unit angular displacement to meet a preset condition, and obtaining a second order spectrum;
and analyzing the second order spectrum by adopting a fault diagnosis algorithm.
2. The method for fault diagnosis of a wind turbine generator set according to claim 1, wherein the number of vibration data per unit angular displacement satisfies a preset condition, and the method comprises:
the number of the vibration data corresponding to each unit angular displacement reaches the maximum number of the vibration data corresponding to a plurality of unit angular displacements; alternatively, the first and second electrodes may be,
the number of the vibration data corresponding to each unit angular displacement reaches a preset number threshold.
3. The method for diagnosing the fault of the wind generating set according to claim 1 or 2, wherein the interpolation processing is performed on the first order spectrum so that the number of the vibration data corresponding to each unit angular displacement meets a preset condition, and a second order spectrum is obtained, and the method comprises the following steps:
and performing interpolation processing on the first order spectrum by adopting a multi-style interpolation algorithm, so that the quantity of vibration data corresponding to each unit angular displacement meets a preset condition, and obtaining a second order spectrum.
4. The method of claim 1, wherein determining an angular displacement of a generator of the wind park according to the current signal comprises:
determining a rotational angular velocity of the generator from the current signal;
determining an angular displacement of the generator based on the angular rotation velocity.
5. The method according to claim 4, wherein the determining a rotational angular velocity of the generator based on the current signal comprises:
reconstructing the current signal by adopting a synchronous compression wavelet technology, and extracting a rotating speed component of the generator;
determining a rotational angular speed of the generator based on the rotational speed component.
6. The method according to claim 5, wherein the reconstructing the current signal by using the synchronous compression wavelet technique, after extracting the rotational speed component of the generator, and before determining the rotational angular speed of the generator according to the rotational speed component, further comprises:
determining the ratio of the rotating speed component in a preset time period to a preset rotating speed interval;
the determining a rotation angular velocity of the generator based on the rotation speed component includes:
and when the occupation ratio is larger than or equal to a preset occupation ratio threshold value, determining the rotation angular speed of the generator according to the rotation speed component.
7. The method of fault diagnosis of a wind park according to claim 1, wherein the angular displacement is characterized by the number of revolutions of the generator;
and/or the current signal is obtained based on current probe detection and/or the current signal is determined based on the output power of the wind generating set;
and/or the vibration data is the vibration data of the driving end of the generator obtained based on the detection of a vibration sensor;
and/or the fault diagnosis algorithm comprises fast Fourier transform and/or envelope analysis and/or wavelet analysis.
8. The method for fault diagnosis of a wind turbine generator set according to claim 1, wherein the wind turbine generator set is a semi-direct drive wind turbine generator set.
9. A fault diagnosis device of a wind turbine generator system, characterized by comprising one or more processors for implementing the fault diagnosis method of any one of claims 1 to 8.
10. A computer-readable storage medium, characterized in that a program is stored thereon, which when executed by a processor, implements the fault diagnosis method according to any one of claims 1 to 8.
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| CN202111210458.6A CN113985276B (en) | 2021-10-18 | 2021-10-18 | Fault diagnosis method and device for wind generating set |
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