What is it about?
The current paper tends to use of power spectral density analysis method of detecting internal race and external race bearing failure in micro wind turbine by estimation stator current signal of the generator. Power spectrum density is effective for detecting the failure in bearing when the MWT rotates at a constant speed, since the speed of MWT variable with the wind speed, it is necessary to apply the technique for fixing the bearing failure characteristic frequencies as indicated by the assessed rotating speed of the MWTG.
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Why is it important?
The failure such as air gap weirdness, rubbing, and scraping between stator and rotor generator arise unavoidably and may cause extremely terrible results for a wind turbine. Therefore, we should pay more attention to detect and identify its cause-bearing failure in a wind turbine to improve the operational reliability
Perspectives
It can be said that the PSDA is a viable technique for identifying the (ERB) and (IRB) failure in micro wind turbines utilizing generator stator current estimations. Bearing failure excited states might a chance to be clearly identified by this method. Consequently, this technique could make promptly utilized to distinguished different types of failure with characteristics of failure in Micro Wind Turbine Generator.
Ali K. Resen
Ministry of Higher Education and Scientific research
Read the Original
This page is a summary of: Bearing failure detection of micro wind turbine via power spectral density analysis for stator current signals spectrum, January 2018, American Institute of Physics,
DOI: 10.1063/1.5039177.
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