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Artificial Neural Network and Kalman Filter for Estimation and Control in Standalone Induction Generator Wind Energy DC Microgrid

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  • Aman A. Tanvir

    (Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, Canada)

  • Adel Merabet

    (Division of Engineering, Saint Mary’s University, Halifax, NS B3H 3C3, Canada)

Abstract

This paper presents an improved estimation strategy for the rotor flux, the rotor speed and the frequency required in the control scheme of a standalone wind energy conversion system based on self-excited three-phase squirrel-cage induction generator with battery storage. At the generator side control, the rotor flux is estimated using an adaptive Kalman filter, and the rotor speed is estimated based on an artificial neural network. This estimation technique enhances the robustness against parametric variations and uncertainties due to the adaptation mechanisms. A vector control scheme is used at the load side converter for controlling the load voltage with respect to amplitude and frequency. The frequency is estimated by a Kalman filter method. The estimation schemes require only voltage and current measurements. A power management system is developed to operate the battery storage in the DC-microgrid based on the wind generation. The control strategy operates under variable wind speed and variable load. The control, estimation and power management schemes are built in the MATLAB/Simulink and RT-LAB platforms and experimentally validated using the OPAL-RT real-time digital controller and a DC-microgrid experimental setup.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:7:p:1743-:d:341773
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    References listed on IDEAS

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    1. Jaramillo-Lopez, Fernando & Kenne, Godpromesse & Lamnabhi-Lagarrigue, Francoise, 2016. "A novel online training neural network-based algorithm for wind speed estimation and adaptive control of PMSG wind turbine system for maximum power extraction," Renewable Energy, Elsevier, vol. 86(C), pages 38-48.
    2. 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.
    3. Bensiali, N. & Etien, E. & Benalia, N., 2015. "Convergence analysis of back-EMF MRAS observers used in sensorless control of induction motor drives," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 115(C), pages 12-23.
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    Cited by:

    1. Weam EL Merrassi & Abdelouahed Abounada & Mohamed Ramzi, 2022. "Performance analysis of novel robust ANN-MRAS observer applied to induction motor drive," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(4), pages 2011-2028, August.
    2. Mohammadali Kiehbadroudinezhad & Adel Merabet & Homa Hosseinzadeh-Bandbafha, 2021. "Optimization of Wind Energy Battery Storage Microgrid by Division Algorithm Considering Cumulative Exergy Demand for Power-Water Cogeneration," Energies, MDPI, vol. 14(13), pages 1-20, June.
    3. Marinka Baghdasaryan & Azatuhi Ulikyan & Arusyak Arakelyan, 2023. "Application of an Artificial Neural Network for Detecting, Classifying, and Making Decisions about Asymmetric Short Circuits in a Synchronous Generator," Energies, MDPI, vol. 16(6), pages 1-19, March.
    4. 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.
    5. Adolfo Dannier & Emanuele Fedele & Ivan Spina & Gianluca Brando, 2022. "Doubly-Fed Induction Generator (DFIG) in Connected or Weak Grids for Turbine-Based Wind Energy Conversion System," Energies, MDPI, vol. 15(17), pages 1-5, September.
    6. José Antonio Cortajarena & Oscar Barambones & Patxi Alkorta & Jon Cortajarena, 2021. "Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid," Mathematics, MDPI, vol. 9(2), pages 1-18, January.
    7. Malgorzata Binek & Andrzej Kanicki & Pawel Rozga, 2021. "Application of an Artificial Neural Network for Measurements of Synchrophasor Indicators in the Power System," Energies, MDPI, vol. 14(9), pages 1-14, April.
    8. Mohammad Soleymannejad & Danial Sadrian Zadeh & Behzad Moshiri & Ebrahim Navid Sadjadi & Jesús García Herrero & Jose Manuel Molina López, 2022. "State Estimation Fusion for Linear Microgrids over an Unreliable Network," Energies, MDPI, vol. 15(6), pages 1-24, March.

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