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A Comparative Performance Analysis of Counter-Rotating Dual-Rotor Wind Turbines with Speed-Adding Increasers

Author

Listed:
  • Radu Saulescu

    (Design of Mechanical Elements and Systems R&D Centre, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Mircea Neagoe

    (Renewable Energy Systems and Recycling R&D Centre, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Codruta Jaliu

    (Renewable Energy Systems and Recycling R&D Centre, Transilvania University of Brasov, 500036 Brasov, Romania)

  • Olimpiu Munteanu

    (Renewable Energy Systems and Recycling R&D Centre, Transilvania University of Brasov, 500036 Brasov, Romania)

Abstract

Increasing the efficiency of wind power conversion into electricity poses major challenges to researchers and developers of wind turbines, who are striving for new solutions that can ensure better use of local wind potential in terms of both feasibility and affordability. The paper proposes a novel concept of wind systems with counter-rotating wind rotors that can integrate either conventional or counter-rotating electric generators, by means of the same differential planetary speed increaser, aiming at providing a comparative analysis of the energy performance of counter-rotating wind turbines with counter-rotating vs. conventional electric generators. To this end, a generalized analytical model for angular speeds and torques has been developed, which can be customized for both system configurations. Three numerical simulation scenarios have been contrasted: (a) a scenario with identical wind rotors in both systems, (b) a scenario with the secondary wind rotors being identical in the two applications, but different from the primary rotors, and (c) a scenario with different secondary rotors in the two wind turbines. The results have shown that the wind systems with counter-rotating generator are more efficient and have a higher amplification ratio, compared to systems with conventional generators. In addition, the analyzed wind system with a counter-rotating generator displays better energy performance with low values for output power and ratio of input speeds, whereas the wind turbine with a conventional generator proves to be more efficient in the high-value range of the above-mentioned parameters.

Suggested Citation

  • Radu Saulescu & Mircea Neagoe & Codruta Jaliu & Olimpiu Munteanu, 2021. "A Comparative Performance Analysis of Counter-Rotating Dual-Rotor Wind Turbines with Speed-Adding Increasers," Energies, MDPI, vol. 14(9), pages 1-21, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:9:p:2594-:d:547766
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    References listed on IDEAS

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    1. Wei-Chiang Hong & Guo-Feng Fan, 2019. "Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting," Energies, MDPI, vol. 12(6), pages 1-16, March.
    2. Mircea Neagoe & Radu Saulescu & Codruta Jaliu & Petru A. Simionescu, 2020. "A Generalized Approach to the Steady-State Efficiency Analysis of Torque-Adding Transmissions Used in Renewable Energy Systems," Energies, MDPI, vol. 13(17), pages 1-18, September.
    3. Guo-Feng Fan & Shan Qing & Hua Wang & Wei-Chiang Hong & Hong-Juan Li, 2013. "Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting," Energies, MDPI, vol. 6(4), pages 1-15, April.
    4. Sebastian Pfaffel & Stefan Faulstich & Kurt Rohrig, 2017. "Performance and Reliability of Wind Turbines: A Review," Energies, MDPI, vol. 10(11), pages 1-27, November.
    5. Radu Saulescu & Mircea Neagoe & Codruta Jaliu, 2018. "Conceptual Synthesis of Speed Increasers for Wind Turbine Conversion Systems," Energies, MDPI, vol. 11(9), pages 1-33, August.
    6. Michał Pacholczyk & Dariusz Karkosiński, 2020. "Parametric Study on a Performance of a Small Counter-Rotating Wind Turbine," Energies, MDPI, vol. 13(15), pages 1-17, July.
    7. Mircea Neagoe & Radu Saulescu & Codruta Jaliu, 2019. "Design and Simulation of a 1 DOF Planetary Speed Increaser for Counter-Rotating Wind Turbines with Counter-Rotating Electric Generators," Energies, MDPI, vol. 12(9), pages 1-19, May.
    8. Farahani, E.M. & Hosseinzadeh, N. & Ektesabi, M., 2012. "Comparison of fault-ride-through capability of dual and single-rotor wind turbines," Renewable Energy, Elsevier, vol. 48(C), pages 473-481.
    9. Jelaska, Damir & Podrug, Srdjan & Perkušić, Milan, 2015. "A novel hybrid transmission for variable speed wind turbines," Renewable Energy, Elsevier, vol. 83(C), pages 78-84.
    10. Filip Kutt & Krzysztof Blecharz & Dariusz Karkosiński, 2020. "Axial-Flux Permanent-Magnet Dual-Rotor Generator for a Counter-Rotating Wind Turbine," Energies, MDPI, vol. 13(11), pages 1-15, June.
    11. Cristina Vázquez-Hernández & Javier Serrano-González & Gabriel Centeno, 2017. "A Market-Based Analysis on the Main Characteristics of Gearboxes Used in Onshore Wind Turbines," Energies, MDPI, vol. 10(11), pages 1-17, October.
    12. Didane, Djamal Hissein & Rosly, Nurhayati & Zulkafli, Mohd Fadhli & Shamsudin, Syariful Syafiq, 2018. "Performance evaluation of a novel vertical axis wind turbine with coaxial contra-rotating concept," Renewable Energy, Elsevier, vol. 115(C), pages 353-361.
    13. Mingming Zhao & Jinchen Ji, 2016. "Dynamic Analysis of Wind Turbine Gearbox Components," Energies, MDPI, vol. 9(2), pages 1-18, February.
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