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Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems

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  • Kouadri, Abdelmalek
  • Hajji, Mansour
  • Harkat, Mohamed-Faouzi
  • Abodayeh, Kamaleldin
  • Mansouri, Majdi
  • Nounou, Hazem
  • Nounou, Mohamed

Abstract

Fault Detection and Diagnosis (FDD) for overall modern Wind Energy Conversion (WEC) systems, particularly its converter, is still a challenge due to the high randomness to their operating environment. This paper presents an advanced FDD approach aims to increase the availability, reliability and required safety of WEC Converters (WECC) under different conditions. The developed FDD approach must be able to detect and correctly diagnose the occurrence of faults in WEC systems. The developed approach exploits the benefits of the machine learning (ML)-based Hidden Markov model (HMM) and the principal component analysis (PCA) model. The PCA technique is used for efficiently extracting and selecting features to be fed to HMM classifier. The effectiveness and higher classification accuracy of the developed PCA-based HMM approach are demonstrated via simulated data collected from the WEC.

Suggested Citation

  • Kouadri, Abdelmalek & Hajji, Mansour & Harkat, Mohamed-Faouzi & Abodayeh, Kamaleldin & Mansouri, Majdi & Nounou, Hazem & Nounou, Mohamed, 2020. "Hidden Markov model based principal component analysis for intelligent fault diagnosis of wind energy converter systems," Renewable Energy, Elsevier, vol. 150(C), pages 598-606.
    • Handle: RePEc:eee:renene:v:150:y:2020:i:c:p:598-606
    DOI: 10.1016/j.renene.2020.01.010
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    References listed on IDEAS

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