Representation Learning for Detecting the Faults in a Wind Turbine Hydraulic Pitch System Using Deep Learning

Panagiotis Korkos, Jaakko Kleemola, Matti Linjama and Arto Lehtovaara
Additional contact information
Panagiotis Korkos: Tribology and Machine Elements, Materials Science and Environmental Engineering, Faculty of Engineering and Natural Sciences, Tampere University, P.O. Box 589, 33014 Tampere, Finland
Jaakko Kleemola: Suomen Hyötytuuli Oy, P.O. Box 305, 28601 Pori, Finland
Matti Linjama: Automation Technology and Mechanical Engineering Unit, Faculty of Engineering and Natural Sciences, Tampere University, P.O. Box 589, 33014 Tampere, Finland
Arto Lehtovaara: Tribology and Machine Elements, Materials Science and Environmental Engineering, Faculty of Engineering and Natural Sciences, Tampere University, P.O. Box 589, 33014 Tampere, Finland

Energies, 2022, vol. 15, issue 24, 1-17

Abstract: Wind turbine operators usually use data from a Supervisory Control and Data Acquisition system to monitor their conditions, but it is challenging to make decisions about maintenance based on hundreds of different parameters. Information is often hidden within measurements that operators are unaware of. Therefore, different feature extraction techniques are recommended. The pitch system is of particular importance, and operators are highly motivated to search for effective monitoring solutions. This study investigated different dimensionality reduction techniques for monitoring a hydraulic pitch system in wind turbines. These techniques include principal component analysis (PCA), kernel PCA and a deep autoencoder. Their effectiveness was evaluated based on the performance of a support vector machine classifier whose input space is the new extracted feature set. The developed methodology has been applied to data from a wind farm consisting of five 2.3 MW fixed-speed onshore wind turbines. The available dataset is composed of nine pitch events representing normal and faulty classes. The results indicate that the features extracted by the deep autoencoder are more informative than those extracted by PCA and kernel PCA. These features led to the achievement of a 95.5% F1-score, proving its superiority over the traditional usage of original features.

Keywords: pitch system; wind turbine; SCADA; fault detection; feature extraction; deep autoencoder (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/24/9279/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/24/9279/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:24:p:9279-:d:996274

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().