DWT-PSD extraction feature for defects diagnosis of small wind generator
Lahcène Noureddine 1  
,  
Mustapha Noureddine 1  
,  
Ahmed Hafaifa 1  
,  
 
 
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Applied Automation & Industrial Diagnostics Laboratory Djelfa University
CORRESPONDING AUTHOR
Lahcène Noureddine   

Applied Automation & Industrial Diagnostics Laboratory Djelfa University
Online publish date: 2019-07-04
Publish date: 2019-07-04
Submission date: 2019-04-09
Final revision date: 2019-06-29
Acceptance date: 2019-07-03
 
Diagnostyka 2019;20(3):45–52
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ABSTRACT
In this work, the ability to detect broken rotor bar defects in small renewable energy system based on squirrel cage induction generator (SCIG), using a digital signal processing of the captured phase currents is presented. The new suggested approach in this study is the combination of two techniques, the first technique is the discrete wavelet transform by decomposition the phase current signal in multilevel frequency bands, with the analysis of some selected approximations and/or details, which containing the both lower and upper sideband components presenting the characteristic frequency of BRB fault, and the second one is the power spectral density (PSD). This approach gives the ability of optimizing the diagnosis of rotor defects in electrical generators. The DWT-PSD results given from the suggested approach are proved and improved by comparing them with the results of PSD obtained from original phase current signal delivered by 5.7 kW squirrel cage induction generator based on small wind energy conversion system.
 
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