DWT-PSD extraction feature for defect diagnosis of small wind generator

• Autorzy: Noureddine Lahcène , Noureddine Mustapha , Hafaifa Ahmed , Kouzou Abdellah

Identyfikatory

DOI

10.29354/diag/110458

• Języki publikacji: EN

Abstrakty

EN

In this paper, the ability to detect broken rotor bar (BRB) defects in a small renewable energy system (based on a squirrel cage induction generator (SCIG)) by the digital signal processing of captured phase currents, is presented. The new approach proposed in this study is a combination of two techniques. The first technique is a discrete wavelet transform (DWT) by the decomposition of the phase current signal in multilevel frequency bands. This is performed with the analysis of some selected approximations and/or details, which contain both the lower and upper sideband components presenting the characteristic frequency of the BRB fault. The second technique is power spectral density (PSD) analysis. This approach provides the ability to optimize the diagnosis of rotor defects in electrical generators. The results obtained by the proposed DWT-PSD approach are proved and improved by comparing them with the results of the PSD analysis, obtained from the original phase current signal delivered by the 5.7-kW squirrel cage induction generator, based on a small wind energy conversion system.

Słowa kluczowe

EN

discrete wavelet transform; BRB defects diagnosis; PSD estimation; renewable energy system

PL

dyskretna transformata falkowa; diagnostyka; wykrywanie defektów; energia odnawialna

• Wydawca: Polskie Towarzystwo Diagnostyki Technicznej
• Czasopismo: Diagnostyka
• Rocznik: 2019
• Tom: Vol. 20, No. 3
• Strony: 45--52
• Opis fizyczny: Bibliogr. 23 poz., rys., tab.

Twórcy

autor

Noureddine Lahcène

• Applied Automation and Industrial Diagnostic Laboratory, Ziane Achour University of Djelfa

autor

Noureddine Mustapha

• Applied Automation and Industrial Diagnostic Laboratory, Ziane Achour University of Djelfa

autor

Hafaifa Ahmed

• Applied Automation and Industrial Diagnostic Laboratory, Ziane Achour University of Djelfa

autor

Kouzou Abdellah

• Applied Automation and Industrial Diagnostic Laboratory, Ziane Achour University of Djelfa

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).