Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection

Camila Correa-Jullian, Sergio Cofre-Martel, Gabriel San Martin, Enrique Lopez Droguett, Gustavo de Novaes Pires Leite and Alexandre Costa
Additional contact information
Camila Correa-Jullian: Garrick Institute for the Risk Sciences, University of California, Los Angeles, CA 90095, USA
Sergio Cofre-Martel: Garrick Institute for the Risk Sciences, University of California, Los Angeles, CA 90095, USA
Gabriel San Martin: Garrick Institute for the Risk Sciences, University of California, Los Angeles, CA 90095, USA
Enrique Lopez Droguett: Garrick Institute for the Risk Sciences, University of California, Los Angeles, CA 90095, USA
Gustavo de Novaes Pires Leite: Federal Institute of Science, Education and Technology Pernambuco (IFPE), Recife 50740-540, PE, Brazil
Alexandre Costa: Center for Renewable Energy from the Federal University of Pernambuco (CER-UFPE), Recife 50740-540, PE, Brazil

Energies, 2022, vol. 15, issue 8, 1-29

Abstract: Driven by the development of machine learning (ML) and deep learning techniques, prognostics and health management (PHM) has become a key aspect of reliability engineering research. With the recent rise in popularity of quantum computing algorithms and public availability of first-generation quantum hardware, it is of interest to assess their potential for efficiently handling large quantities of operational data for PHM purposes. This paper addresses the application of quantum kernel classification models for fault detection in wind turbine systems (WTSs). The analyzed data correspond to low-frequency SCADA sensor measurements and recorded SCADA alarm logs, focused on the early detection of pitch fault failures. This work aims to explore potential advantages of quantum kernel methods, such as quantum support vector machines (Q-SVMs), over traditional ML approaches and compare principal component analysis (PCA) and autoencoders (AE) as feature reduction tools. Results show that the proposed quantum approach is comparable to conventional ML models in terms of performance and can outperform traditional models (random forest, k-nearest neighbors) for the selected reduced dimensionality of 19 features for both PCA and AE. The overall highest mean accuracies obtained are 0.945 for Gaussian SVM and 0.925 for Q-SVM models.

Keywords: quantum machine learning; quantum kernels; wind turbine systems; SCADA system; pitch fault diagnostics; feature reduction; principal component analysis; autoencoders; machine learning; prognostics and health management (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: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/8/2792/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/8/2792/ (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:8:p:2792-:d:791286

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 ().