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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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    1. Francesc Pozo & Yolanda Vidal & Josep M. Serrahima, 2016. "On Real-Time Fault Detection in Wind Turbines: Sensor Selection Algorithm and Detection Time Reduction Analysis," Energies, MDPI, vol. 9(7), pages 1-22, July.
    2. Pere Marti-Puig & Alejandro Blanco-M & Juan José Cárdenas & Jordi Cusidó & Jordi Solé-Casals, 2019. "Feature Selection Algorithms for Wind Turbine Failure Prediction," Energies, MDPI, vol. 12(3), pages 1-18, January.
    3. Wu, Yueqi & Ma, Xiandong, 2022. "A hybrid LSTM-KLD approach to condition monitoring of operational wind turbines," Renewable Energy, Elsevier, vol. 181(C), pages 554-566.
    4. Cristian Velandia-Cardenas & Yolanda Vidal & Francesc Pozo, 2021. "Wind Turbine Fault Detection Using Highly Imbalanced Real SCADA Data," Energies, MDPI, vol. 14(6), pages 1-26, March.
    5. Conor McKinnon & James Carroll & Alasdair McDonald & Sofia Koukoura & Charlie Plumley, 2021. "Investigation of Isolation Forest for Wind Turbine Pitch System Condition Monitoring Using SCADA Data," Energies, MDPI, vol. 14(20), pages 1-20, October.
    6. Md Liton Hossain & Ahmed Abu-Siada & S. M. Muyeen, 2018. "Methods for Advanced Wind Turbine Condition Monitoring and Early Diagnosis: A Literature Review," Energies, MDPI, vol. 11(5), pages 1-14, May.
    7. Qiu, Yingning & Feng, Yanhui & Infield, David, 2020. "Fault diagnosis of wind turbine with SCADA alarms based multidimensional information processing method," Renewable Energy, Elsevier, vol. 145(C), pages 1923-1931.
    8. Yingying Zhao & Dongsheng Li & Ao Dong & Dahai Kang & Qin Lv & Li Shang, 2017. "Fault Prediction and Diagnosis of Wind Turbine Generators Using SCADA Data," Energies, MDPI, vol. 10(8), pages 1-17, August.
    9. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Ken Bruton & Dominic T. J. O’Sullivan, 2018. "A Robust Prescriptive Framework and Performance Metric for Diagnosing and Predicting Wind Turbine Faults Based on SCADA and Alarms Data with Case Study," Energies, MDPI, vol. 11(7), pages 1-21, July.
    10. Habibi, Hamed & Howard, Ian & Simani, Silvio, 2019. "Reliability improvement of wind turbine power generation using model-based fault detection and fault tolerant control: A review," Renewable Energy, Elsevier, vol. 135(C), pages 877-896.
    11. Chen, Hansi & Liu, Hang & Chu, Xuening & Liu, Qingxiu & Xue, Deyi, 2021. "Anomaly detection and critical SCADA parameters identification for wind turbines based on LSTM-AE neural network," Renewable Energy, Elsevier, vol. 172(C), pages 829-840.
    12. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
    13. Correa-Jullian, Camila & Cardemil, José Miguel & López Droguett, Enrique & Behzad, Masoud, 2020. "Assessment of Deep Learning techniques for Prognosis of solar thermal systems," Renewable Energy, Elsevier, vol. 145(C), pages 2178-2191.
    14. Stetco, Adrian & Dinmohammadi, Fateme & Zhao, Xingyu & Robu, Valentin & Flynn, David & Barnes, Mike & Keane, John & Nenadic, Goran, 2019. "Machine learning methods for wind turbine condition monitoring: A review," Renewable Energy, Elsevier, vol. 133(C), pages 620-635.
    15. Cho, Seongpil & Choi, Minjoo & Gao, Zhen & Moan, Torgeir, 2021. "Fault detection and diagnosis of a blade pitch system in a floating wind turbine based on Kalman filters and artificial neural networks," Renewable Energy, Elsevier, vol. 169(C), pages 1-13.
    16. Cho, Seongpil & Gao, Zhen & Moan, Torgeir, 2018. "Model-based fault detection, fault isolation and fault-tolerant control of a blade pitch system in floating wind turbines," Renewable Energy, Elsevier, vol. 120(C), pages 306-321.
    17. Francesc Pozo & Yolanda Vidal, 2015. "Wind Turbine Fault Detection through Principal Component Analysis and Statistical Hypothesis Testing," Energies, MDPI, vol. 9(1), pages 1-20, December.
    18. Kevin Leahy & Colm Gallagher & Peter O’Donovan & Dominic T. J. O’Sullivan, 2019. "Issues with Data Quality for Wind Turbine Condition Monitoring and Reliability Analyses," Energies, MDPI, vol. 12(2), pages 1-22, January.
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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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