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Vibration fault diagnosis method of wind power generator and storage medium

A wind turbine and fault diagnosis technology, which is applied in the direction of engine testing, computer components, machine/structural components testing, etc., can solve problems such as fault analysis mode aliasing

Inactive Publication Date: 2019-08-20
PUTIAN UNIV
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Problems solved by technology

[0004] Therefore, it is necessary to provide a wind turbine vibration fault diagnosis method and storage medium to solve the problem of modal aliasing in fault analysis based on empirical mode decomposition

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  • Vibration fault diagnosis method of wind power generator and storage medium
  • Vibration fault diagnosis method of wind power generator and storage medium

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Embodiment Construction

[0048] In order to explain in detail the technical content, structural features, achieved goals and effects of the technical solution, the following will be described in detail in conjunction with specific embodiments and accompanying drawings.

[0049] see figure 1 , the method for diagnosing a vibration fault of a wind power generator described in the present embodiment includes the following steps:

[0050] Step S110: Obtain the vibration signal f(t) of the wind generator; the vibration signal of the wind generator can be obtained through the vibration sensor, and the fault occurs in different parts of the wind generator, such as the inner ring of the bearing, the outer ring of the bearing, and the rolling body. Fault; when the wind turbine is working, the vibration sensor can obtain the vibration signals of the wind turbine bearing inner ring, bearing outer ring and rolling elements.

[0051] Step S120: Decompose the obtained vibration signal by the variational mode decom...

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Abstract

The invention relates to a vibration fault diagnosis method of a wind power generator and a storage medium. The method comprises the following steps of acquiring a vibration signal f(t) of the wind power generator; decomposing the acquired vibration signal to k mode components uk(t) by a variation mode decomposition algorithm; calculating energy entropies of the k mode components, and constructingcharacteristics vectors according to the energy entropy of each mode; and inputting the characteristic vectors into a model supporting a vector machine for fault identification. The variation mode decomposition algorithm is used for decomposing the vibration signal to a non-recursion and variation mode decomposition mode, the mode belongs to the problem of function optimization under the constraint condition, minimum bandwidth estimated by each mode is used as a target, each mode function and a corresponding central frequency are updated, the target is essentially a group of self-adaptive wiener filters, the vibration fault information still can be accurately decomposed compared with EMD, and the problem of mode mixing in EMD decomposition can be effectively prevented.

Description

technical field [0001] The invention relates to the technical field of wind power generators, in particular to a method for diagnosing vibration faults of a wind power generator and a storage medium. Background technique [0002] Today's traditional energy is becoming more and more scarce, and the environment and climate are getting worse and worse. The utilization and development of new energy has become the focus of attention of all countries. Wind energy is a renewable green energy that has been vigorously developed, researched and utilized by many countries. However, with the large-scale construction, operation and production of wind turbines, a series of new technical and environmental problems have also emerged. There is an urgent need to study related wind power monitoring and fault diagnosis technologies. [0003] Using effective equipment monitoring and fault diagnosis methods, it can continuously monitor various parameters of wind turbine operation, track various...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M15/00G06K9/62
CPCG01M15/00G06F18/2411G06F18/214
Inventor 陈学军吴展鸿杨栋林亚君
Owner PUTIAN UNIV
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