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Performance Improvement for Small-Scale Wind Turbine System Based on Maximum Power Point Tracking Control

Author

Listed:
  • Ramadoni Syahputra

    (Department of Electrical Engineering, Faculty of Engineering, Universitas Muhammadiyah Yogyakarta, Yogyakarta 55183, Indonesia)

  • Indah Soesanti

    (Department of Electrical Engineering and Information Technology, Faculty of Engineering, Universitas Gadjah Mada, Yogyakarta 55281, Indonesia)

Abstract

This paper proposes a strategy for performance improvement of small-scale wind turbine systems using maximum power point tracking control (MPPT). In this study, wind-turbine systems which use permanent magnet synchronous generators and converter devices are modeled in Simulink-Matlab software. In order to increase the power generated, MPPT is used based on the extended perturb and observe (PO) method. This algorithm has the ability to improve the speed of the turbine without oscillation. To analyze the ability of the PO-based MPPT in maximizing output power, performance examination of wind turbine systems in Simulink-Matlab software was conducted. The study is carried out with a 3000 W wind turbine device serving various electrical loads of 50 Ω, 100 Ω, 200 Ω, and 300 Ω, and each ohm varies with a wind speed of 4, 5, 6.5, 7, 8.5, 9, and 10 m/s. The overall turbine system performance found that the maximum increase in system output power occurs when it is loaded with 200 Ω with a wind speed of 6.5 m/s. During this combination of 200 Ω and 6.5 m/s, there are high increments of output power at 135.62% caused by the installation of MPPT controllers, with an average output power increase of 50.77%. The results of this study proved that PO-based MPPT has successfully improved the performance of wind-turbine systems.

Suggested Citation

  • Ramadoni Syahputra & Indah Soesanti, 2019. "Performance Improvement for Small-Scale Wind Turbine System Based on Maximum Power Point Tracking Control," Energies, MDPI, vol. 12(20), pages 1-18, October.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:20:p:3938-:d:277382
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    Citations

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    Cited by:

    1. Diego Calabrese & Gioacchino Tricarico & Elia Brescia & Giuseppe Leonardo Cascella & Vito Giuseppe Monopoli & Francesco Cupertino, 2020. "Variable Structure Control of a Small Ducted Wind Turbine in the Whole Wind Speed Range Using a Luenberger Observer," Energies, MDPI, vol. 13(18), pages 1-23, September.
    2. Tania García-Sánchez & Arbinda Kumar Mishra & Elías Hurtado-Pérez & Rubén Puché-Panadero & Ana Fernández-Guillamón, 2020. "A Controller for Optimum Electrical Power Extraction from a Small Grid-Interconnected Wind Turbine," Energies, MDPI, vol. 13(21), pages 1-16, November.
    3. Guilherme Ferreira de Lima & William de Jesus Kremes & Hugo Valadares Siqueira & Bahar Aliakbarian & Attilio Converti & Carlos Henrique Illa Font, 2023. "A Three-Phase Phase-Modular Single-Ended Primary-Inductance Converter Rectifier Operating in Discontinuous Conduction Mode for Small-Scale Wind Turbine Applications," Energies, MDPI, vol. 16(13), pages 1-18, July.
    4. Sławomir Karyś & Paweł Stawczyk, 2021. "Cost-Effective Power Converters for Small Wind Turbines," Energies, MDPI, vol. 14(18), pages 1-14, September.
    5. Mohammed Yousri Silaa & Mohamed Derbeli & Oscar Barambones & Ali Cheknane, 2020. "Design and Implementation of High Order Sliding Mode Control for PEMFC Power System," Energies, MDPI, vol. 13(17), pages 1-15, August.
    6. Tavakol Aghaei, Vahid & Ağababaoğlu, Arda & Bawo, Biram & Naseradinmousavi, Peiman & Yıldırım, Sinan & Yeşilyurt, Serhat & Onat, Ahmet, 2023. "Energy optimization of wind turbines via a neural control policy based on reinforcement learning Markov chain Monte Carlo algorithm," Applied Energy, Elsevier, vol. 341(C).
    7. Stanisław Chudzik, 2023. "Wind Microturbine with Adjustable Blade Pitch Angle," Energies, MDPI, vol. 16(2), pages 1-16, January.

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