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Sensorless Active and Reactive Control for DFIG Wind Turbines Using Opposition-Based Learning Technique

Author

Listed:
  • Ali Mohamed Eltamaly

    (Sustainable Energy Technologies Center, King Saud University, Riyadh 11421, Saudi Arabia
    Electrical Engineering Department, Mansoura University, Mansoura 35516, Egypt
    K.A. CARE Energy Research and Innovation Center, Riyadh 11451, Saudi Arabia)

  • Mamdooh Al-Saud

    (Electrical Engineering Department, College of Engineering, King Saud University, Riyadh 11421, Saudi Arabia)

  • Khairy Sayed

    (Electrical Engineering Department, Faculty of Engineering, Sohag University, Sohag 82524, Egypt)

  • Ahmed G. Abo-Khalil

    (Department of Electrical Engineering, College of Engineering, Majmaah University, Almajmaah 11952, Saudi Arabia
    Department of Electrical Engineering, College of Engineering, Assuit University, Assuit 71515, Egypt)

Abstract

In this paper, a wind speed sensorless control method for doubly-fed induction generator (DFIG) control in wind energy systems is proposed. This method is based on using opposition-based learning (OBL) in optimizing the parameters of the support vector regression (SVR) algorithm. These parameters are tuned by applying particle swarm optimization (PSO) method. As a general rule, wind speed measurements are usually done using an anemometer. The measured wind speed by the anemometer is taken at the level of the blades. In a high-power wind turbine, the blade diameter is very large which makes the measurement of the wind speed at a single point inaccurate. Moreover, using anemometers also increases the maintenance cost, complexity and the system cost. Therefore, estimating the wind speed in variable speed wind power systems gives a precise amount of wind speed which is then used in the generator control. The proposed method uses the generator characteristics in mapping a relationship between the generated power, rotational speed and wind speed. This process is carried on off-line and the relationship is then used online to deduce the wind speed based on the obtained relationship. Using OBL with PSO-SVR to tune the SVR parameters accelerates the process to get the optimum parameters in different wind speeds.

Suggested Citation

  • Ali Mohamed Eltamaly & Mamdooh Al-Saud & Khairy Sayed & Ahmed G. Abo-Khalil, 2020. "Sensorless Active and Reactive Control for DFIG Wind Turbines Using Opposition-Based Learning Technique," Sustainability, MDPI, vol. 12(9), pages 1-14, April.
    • Handle: RePEc:gam:jsusta:v:12:y:2020:i:9:p:3583-:d:351570
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    References listed on IDEAS

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    1. Ahmed G. Abo-Khalil & Ali S. Alghamdi & Ali M. Eltamaly & M. S. Al-Saud & Praveen R. P. & Khairy Sayed & G. R. Bindu & Iskander Tlili, 2019. "Design of State Feedback Current Controller for Fast Synchronization of DFIG in Wind Power Generation Systems," Energies, MDPI, vol. 12(12), pages 1-26, June.
    2. Ahmed G. Abo-Khalil & Saeed Alyami & Khairy Sayed & Ayman Alhejji, 2019. "Dynamic Modeling of Wind Turbines Based on Estimated Wind Speed under Turbulent Conditions," Energies, MDPI, vol. 12(10), pages 1-25, May.
    3. Ahmed Abo-Khalil, 2013. "Impacts of Wind Farms on Power System Stability," Chapters, in: S. M. Muyeen & Ahmed Al-Durra (ed.), Modeling and Control Aspects of Wind Power Systems, IntechOpen.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Mohamed Abdelrahem & Christoph Hackl & Ralph Kennel & Jose Rodriguez, 2021. "Low Sensitivity Predictive Control for Doubly-Fed Induction Generators Based Wind Turbine Applications," Sustainability, MDPI, vol. 13(16), pages 1-13, August.
    2. Ahmed G. Abo-Khalil & Ali M. Eltamaly & Praveen R.P. & Ali S. Alghamdi & Iskander Tlili, 2020. "A Sensorless Wind Speed and Rotor Position Control of PMSG in Wind Power Generation Systems," Sustainability, MDPI, vol. 12(20), pages 1-19, October.
    3. Ahmed G. Abo-Khalil & Ali S. Alghamdi, 2021. "MPPT of Permanent Magnet Synchronous Generator in Tidal Energy Systems Using Support Vector Regression," Sustainability, MDPI, vol. 13(4), pages 1-15, February.
    4. A. Padmaja & Allusivala Shanmukh & Siva Subrahmanyam Mendu & Ramesh Devarapalli & Javier Serrano González & Fausto Pedro García Márquez, 2021. "Design of Capacitive Bridge Fault Current Limiter for Low-Voltage Ride-Through Capacity Enrichment of Doubly Fed Induction Generator-Based Wind Farm," Sustainability, MDPI, vol. 13(12), pages 1-17, June.
    5. Abdulaziz Almutairi & Ahmed G. Abo-Khalil & Khairy Sayed & Naif Albagami, 2020. "MPPT for a PV Grid-Connected System to Improve Efficiency under Partial Shading Conditions," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    6. Habib Benbouhenni & Nicu Bizon & Ilhami Colak & Phatiphat Thounthong & Noureddine Takorabet, 2022. "Simplified Super Twisting Sliding Mode Approaches of the Double-Powered Induction Generator-Based Multi-Rotor Wind Turbine System," Sustainability, MDPI, vol. 14(9), pages 1-22, April.
    7. Ahmed G. Abo-Khalil & Walied Alharbi & Abdel-Rahman Al-Qawasmi & Mohammad Alobaid & Ibrahim M. Alarifi, 2021. "Maximum Power Point Tracking of PV Systems under Partial Shading Conditions Based on Opposition-Based Learning Firefly Algorithm," Sustainability, MDPI, vol. 13(5), pages 1-18, March.
    8. Ramesh Kumar Behara & Akshay Kumar Saha, 2022. "Artificial Intelligence Control System Applied in Smart Grid Integrated Doubly Fed Induction Generator-Based Wind Turbine: A Review," Energies, MDPI, vol. 15(17), pages 1-56, September.
    9. Ahmed Sobhy & Ahmed G. Abo-Khalil & Dong Lei & Tareq Salameh & Adel Merabet & Malek Alkasrawi, 2022. "Coupling DFIG-Based Wind Turbines with the Grid under Voltage Imbalance Conditions," Sustainability, MDPI, vol. 14(9), pages 1-20, April.
    10. Omar Alrumayh & Khairy Sayed & Abdulaziz Almutairi, 2023. "LVRT and Reactive Power/Voltage Support of Utility-Scale PV Power Plants during Disturbance Conditions," Energies, MDPI, vol. 16(7), pages 1-20, April.

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