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

Author

Listed:
  • 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
    Department of Civil and Environmental Engineering, 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
    Center for Renewable Energy from the Federal University of Pernambuco (CER-UFPE), Recife 50740-540, PE, Brazil)

  • Alexandre Costa

    (Center for Renewable Energy from the Federal University of Pernambuco (CER-UFPE), Recife 50740-540, PE, Brazil)

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.

Suggested Citation

  • Camila Correa-Jullian & Sergio Cofre-Martel & Gabriel San Martin & Enrique Lopez Droguett & Gustavo de Novaes Pires Leite & Alexandre Costa, 2022. "Exploring Quantum Machine Learning and Feature Reduction Techniques for Wind Turbine Pitch Fault Detection," Energies, MDPI, vol. 15(8), pages 1-29, April.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:8:p:2792-:d:791286
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    References listed on IDEAS

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    Cited by:

    1. Fang Gao & Guojian Wu, 2023. "Application of Quantum Computing in Power Systems," Energies, MDPI, vol. 16(5), pages 1-3, February.

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