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Adaptive-tuning of extended Kalman filter used for small scale wind generator control

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  • Al-Ghossini, Hossam
  • Locment, Fabrice
  • Sechilariu, Manuela
  • Gagneur, Laurent
  • Forgez, Christophe

Abstract

In this paper a small scale wind generator based on a permanent magnet synchronous machine (PMSM) and associated with an indirect maximum power point tracking (MPPT) algorithm is proposed. Choosing an energy conversion active structure and a sensorless PMSM, to control the system, a speed estimator is required. Facing to other methods, the extended Kalman filter (EKF) model-based estimator allows sensorless drive control in a wide speed range and estimates the rotation speed with a rapid response. The EKF parameters tuning is solved by introducing an adaptive method, i.e. adaptive-tuning EKF. This adaptive estimation approach is innovative by using a covariance matching technique. The experimental results prove that the proposed method is technically feasible with good performances within some limits.

Suggested Citation

  • Al-Ghossini, Hossam & Locment, Fabrice & Sechilariu, Manuela & Gagneur, Laurent & Forgez, Christophe, 2016. "Adaptive-tuning of extended Kalman filter used for small scale wind generator control," Renewable Energy, Elsevier, vol. 85(C), pages 1237-1245.
    • Handle: RePEc:eee:renene:v:85:y:2016:i:c:p:1237-1245
    DOI: 10.1016/j.renene.2015.07.073
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    References listed on IDEAS

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    1. Narayana, M. & Putrus, G.A. & Jovanovic, M. & Leung, P.S. & McDonald, S., 2012. "Generic maximum power point tracking controller for small-scale wind turbines," Renewable Energy, Elsevier, vol. 44(C), pages 72-79.
    2. Urtasun, Andoni & Sanchis, Pablo & San Martín, Idoia & López, Jesús & Marroyo, Luis, 2013. "Modeling of small wind turbines based on PMSG with diode bridge for sensorless maximum power tracking," Renewable Energy, Elsevier, vol. 55(C), pages 138-149.
    3. Senjyu, Tomonobu & Ochi, Yasutaka & Kikunaga, Yasuaki & Tokudome, Motoki & Yona, Atsushi & Muhando, Endusa Billy & Urasaki, Naomitsu & Funabashi, Toshihisa, 2009. "Sensor-less maximum power point tracking control for wind generation system with squirrel cage induction generator," Renewable Energy, Elsevier, vol. 34(4), pages 994-999.
    4. Kortabarria, Iñigo & Andreu, Jon & Martínez de Alegría, Iñigo & Jiménez, Jaime & Gárate, José Ignacio & Robles, Eider, 2014. "A novel adaptative maximum power point tracking algorithm for small wind turbines," Renewable Energy, Elsevier, vol. 63(C), pages 785-796.
    5. Kot, R. & Rolak, M. & Malinowski, M., 2013. "Comparison of maximum peak power tracking algorithms for a small wind turbine," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 91(C), pages 29-40.
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    Cited by:

    1. 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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