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Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System

Author

Listed:
  • Yanis Hamoudi

    (Laboratoire de Maitrise des Energies Renouvelables (LMER), Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria)

  • Hocine Amimeur

    (Laboratoire de Maitrise des Energies Renouvelables (LMER), Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria)

  • Djamal Aouzellag

    (Laboratoire de Maitrise des Energies Renouvelables (LMER), Faculté de Technologie, Université de Bejaia, Bejaia 06000, Algeria)

  • Maher G. M. Abdolrasol

    (Department of Electric, Electronics and System Engineering, Faculty of Engineering and Built Environment, Universiti Kebangsaan Malaysia, Bangi 43600, Malaysia)

  • Taha Selim Ustun

    (Fukushima Renewable Energy Institute, AIST (FREA), National Institute of Advanced Industrial Science and Technology (AIST), Koriyama 963-0298, Japan)

Abstract

This paper introduces a novel approach to speed-sensorless predictive torque control (PTC) in an autonomous wind energy conversion system, specifically utilizing an asymmetric double star induction generator (ADSIG). To achieve accurate estimation of non-linear quantities, the Gaussian Process Regression algorithm (GPR) is employed as a powerful machine learning tool for designing speed and flux estimators. To enhance the capabilities of the GPR, two improvements were implemented, (a) hyperparametric optimization through the Bayesian optimization (BO) algorithm and (b) curation of the input vector using the gray box concept, leveraging our existing knowledge of the ADSIG. Simulation results have demonstrated that the proposed GPR-PTC would remain robust and unaffected by the absence of a speed sensor, maintaining performance even under varying magnetizing inductance. This enables a reliable and cost-effective control solution.

Suggested Citation

  • Yanis Hamoudi & Hocine Amimeur & Djamal Aouzellag & Maher G. M. Abdolrasol & Taha Selim Ustun, 2023. "Hyperparameter Bayesian Optimization of Gaussian Process Regression Applied in Speed-Sensorless Predictive Torque Control of an Autonomous Wind Energy Conversion System," Energies, MDPI, vol. 16(12), pages 1-19, June.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:12:p:4738-:d:1172072
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    References listed on IDEAS

    as
    1. Fengxiang Wang & Zhenbin Zhang & Xuezhu Mei & José Rodríguez & Ralph Kennel, 2018. "Advanced Control Strategies of Induction Machine: Field Oriented Control, Direct Torque Control and Model Predictive Control," Energies, MDPI, vol. 11(1), pages 1-13, January.
    2. Aman A. Tanvir & Adel Merabet, 2020. "Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid," Energies, MDPI, vol. 13(7), pages 1-16, April.
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    4. Nitesh Kumar Singh & Chaitali Koley & Sadhan Gope & Subhojit Dawn & Taha Selim Ustun, 2021. "An Economic Risk Analysis in Wind and Pumped Hydro Energy Storage Integrated Power System Using Meta-Heuristic Algorithm," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    5. Arup Das & Subhojit Dawn & Sadhan Gope & Taha Selim Ustun, 2022. "A Strategy for System Risk Mitigation Using FACTS Devices in a Wind Incorporated Competitive Power System," Sustainability, MDPI, vol. 14(13), pages 1-21, July.
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