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

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

Research output: Contribution to journalArticle

3 Citations (Scopus)

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. The obtained results demonstrate the efficiency of the PCA-based HMM method over the PCA-based support vector machine (SVM) method. The comparison is made based on several performance metrics through different operating conditions of the WEC systems.

Original languageEnglish
Pages (from-to)598-606
Number of pages9
JournalRenewable Energy
Volume150
DOIs
Publication statusPublished - May 2020

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Keywords

  • Fault Detection and Diagnosis (FDD)
  • Hidden Markov Model (HMM)
  • Machine Learning (ML)
  • Principal Component Analysis (PCA)
  • Wind Energy Conversion Converter (WECC) Systems

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment

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