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 and Mohamed Nounou

Renewable Energy, 2020, vol. 150, issue C, 598-606

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.

Keywords: Machine Learning (ML); Hidden Markov Model (HMM); Principal Component Analysis (PCA); Wind Energy Conversion Converter (WECC) Systems; Fault Detection and Diagnosis (FDD) (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:150:y:2020:i:c:p:598-606

DOI: 10.1016/j.renene.2020.01.010

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