IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i5p762-d760013.html
   My bibliography  Save this article

Duty Cycle-Rotor Angular Speed Reverse Acting Relationship Steady State Analysis Based on a PMSG d–q Transform Modeling

Author

Listed:
  • José Genaro González-Hernández

    (Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Av. Primero de Mayo, Ciudad Madero 89440, Tamaulipas, Mexico
    Mechatronics and Renewable Energy Department, Universidad Tecnológica de Altamira, Altamira 89608, Tamaulipas, Mexico)

  • Rubén Salas-Cabrera

    (Tecnológico Nacional de México/Instituto Tecnológico de Ciudad Madero, Av. Primero de Mayo, Ciudad Madero 89440, Tamaulipas, Mexico)

Abstract

Multilevel converters have been broadly used in wind energy conversion systems (WECS) to set the generator angular speed to a certain value, which allows maximizing wind power extraction; nevertheless, power that is drawn out from WECS strongly depends on the power coefficient and the ability to operate at the optimal tip speed ratio that corresponds to the maximum power coefficient. This work presents a novel and formal steady-state analysis to demonstrate the reverse relationship between the duty cycle of a multilevel boost converter (MBC) and the angular speed of a permanent magnet synchronous generator (PMSG). The study was based on the d–q transformation using the rotor reference frame. It was carried out by employing a reduced order dynamic system that included an equivalent electrical load resistance as a representation for the subsystems that were cascade-connected at the terminals of the PMSG. The steady-state characteristic was obtained by using the definition of equilibrium point. The set of nonlinear equations that represents the steady state of this WECS was solved by using the Newton method; besides, an analysis that considers the equivalent load as a bifurcation parameter demonstrates that the number of equilibria never changes.

Suggested Citation

  • José Genaro González-Hernández & Rubén Salas-Cabrera, 2022. "Duty Cycle-Rotor Angular Speed Reverse Acting Relationship Steady State Analysis Based on a PMSG d–q Transform Modeling," Mathematics, MDPI, vol. 10(5), pages 1-17, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:762-:d:760013
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/5/762/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/5/762/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Alhmoud, Lina & Wang, Bingsen, 2018. "A review of the state-of-the-art in wind-energy reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 1643-1651.
    2. Nikolić, Vlastimir & Sajjadi, Shahin & Petković, Dalibor & Shamshirband, Shahaboddin & Ćojbašić, Žarko & Por, Lip Yee, 2016. "Design and state of art of innovative wind turbine systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 258-265.
    3. Khamlaj, Tariq Abdulsalam & Rumpfkeil, Markus Peer, 2018. "Analysis and optimization of ducted wind turbines," Energy, Elsevier, vol. 162(C), pages 1234-1252.
    4. Hu, Lu & Xue, Fei & Qin, Zijian & Shi, Jiying & Qiao, Wen & Yang, Wenjing & Yang, Ting, 2019. "Sliding mode extremum seeking control based on improved invasive weed optimization for MPPT in wind energy conversion system," Applied Energy, Elsevier, vol. 248(C), pages 567-575.
    5. Chen, Jian & Yao, Wei & Zhang, Chuan-Ke & Ren, Yaxing & Jiang, Lin, 2019. "Design of robust MPPT controller for grid-connected PMSG-Based wind turbine via perturbation observation based nonlinear adaptive control," Renewable Energy, Elsevier, vol. 134(C), pages 478-495.
    6. Njiri, Jackson G. & Söffker, Dirk, 2016. "State-of-the-art in wind turbine control: Trends and challenges," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 377-393.
    7. Jiang, Haibo & Li, Yanru & Cheng, Zhongqing, 2015. "Performances of ideal wind turbine," Renewable Energy, Elsevier, vol. 83(C), pages 658-662.
    8. Sun, Haiying & Qiu, Changyu & Lu, Lin & Gao, Xiaoxia & Chen, Jian & Yang, Hongxing, 2020. "Wind turbine power modelling and optimization using artificial neural network with wind field experimental data," Applied Energy, Elsevier, vol. 280(C).
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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).
    2. Li, Tenghui & Yang, Jin & Ioannou, Anastasia, 2024. "Data-driven control of wind turbine under online power strategy via deep learning and reinforcement learning," Renewable Energy, Elsevier, vol. 234(C).
    3. Zouheyr, Dekali & Lotfi, Baghli & Abdelmadjid, Boumediene, 2021. "Improved hardware implementation of a TSR based MPPT algorithm for a low cost connected wind turbine emulator under unbalanced wind speeds," Energy, Elsevier, vol. 232(C).
    4. Moss, Coleman & Maulik, Romit & Iungo, Giacomo Valerio, 2024. "Augmenting insights from wind turbine data through data-driven approaches," Applied Energy, Elsevier, vol. 376(PA).
    5. Manisha Sawant & Sameer Thakare & A. Prabhakara Rao & Andrés E. Feijóo-Lorenzo & Neeraj Dhanraj Bokde, 2021. "A Review on State-of-the-Art Reviews in Wind-Turbine- and Wind-Farm-Related Topics," Energies, MDPI, vol. 14(8), pages 1-30, April.
    6. Mousavi, Yashar & Bevan, Geraint & Kucukdemiral, Ibrahim Beklan & Fekih, Afef, 2022. "Sliding mode control of wind energy conversion systems: Trends and applications," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    7. Yao, Ganzhou & Luo, Zirong & Lu, Zhongyue & Wang, Mangkuan & Shang, Jianzhong & Guerrerob, Josep M., 2023. "Unlocking the potential of wave energy conversion: A comprehensive evaluation of advanced maximum power point tracking techniques and hybrid strategies for sustainable energy harvesting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
    8. Bon-Yong Koo & Dae-Yi Jung, 2019. "A Comparative Study on Primary Bearing Rating Life of a 5-MW Two-Blade Wind Turbine System Based on Two Different Control Domains," Energies, MDPI, vol. 12(13), pages 1-16, July.
    9. N. Aravindhan & M. P. Natarajan & S. Ponnuvel & P.K. Devan, 2023. "Recent developments and issues of small-scale wind turbines in urban residential buildings- A review," Energy & Environment, , vol. 34(4), pages 1142-1169, June.
    10. Mojtaba Nasiri & Saleh Mobayen & Quan Min Zhu, 2019. "Super-Twisting Sliding Mode Control for Gearless PMSG-Based Wind Turbine," Complexity, Hindawi, vol. 2019, pages 1-15, April.
    11. Igliński, Bartłomiej & Iglińska, Anna & Koziński, Grzegorz & Skrzatek, Mateusz & Buczkowski, Roman, 2016. "Wind energy in Poland – History, current state, surveys, Renewable Energy Sources Act, SWOT analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 64(C), pages 19-33.
    12. Aqachmar, Zineb & Allouhi, Amine & Jamil, Abdelmajid & Gagouch, Belgacem & Kousksou, Tarik, 2019. "Parabolic trough solar thermal power plant Noor I in Morocco," Energy, Elsevier, vol. 178(C), pages 572-584.
    13. Sun, Shilin & Wang, Tianyang & Chu, Fulei, 2022. "In-situ condition monitoring of wind turbine blades: A critical and systematic review of techniques, challenges, and futures," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    14. Anto Anbarasu Yesudhas & Young Hoon Joo & Seong Ryong Lee, 2022. "Reference Model Adaptive Control Scheme on PMVG-Based WECS for MPPT under a Real Wind Speed," Energies, MDPI, vol. 15(9), pages 1-17, April.
    15. Li, Rui & Zhang, Jincheng & Zhao, Xiaowei, 2022. "Dynamic wind farm wake modeling based on a Bilateral Convolutional Neural Network and high-fidelity LES data," Energy, Elsevier, vol. 258(C).
    16. Huazhen Cao & Chong Gao & Xuan He & Yang Li & Tao Yu, 2020. "Multi-Agent Cooperation Based Reduced-Dimension Q(?) Learning for Optimal Carbon-Energy Combined-Flow," Energies, MDPI, vol. 13(18), pages 1-22, September.
    17. Xiang Li & Jing Qian & Danning Tian & Yun Zeng & Fei Cao & Lisheng Li & Ganyuan Zhang, 2023. "Maximum Power Tracking Control of Wind Turbines Based on a New Prescribed Performance Function," Energies, MDPI, vol. 16(10), pages 1-21, May.
    18. Kadoche, Elie & Gourvénec, Sébastien & Pallud, Maxime & Levent, Tanguy, 2023. "MARLYC: Multi-Agent Reinforcement Learning Yaw Control," Renewable Energy, Elsevier, vol. 217(C).
    19. Papini, Guglielmo & Faedo, Nicolás & Mattiazzo, Giuliana, 2024. "Fault diagnosis and fault-tolerant control in wave energy: A perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    20. Abdel Hameed, Hossam S. & Hashem, Islam & Nawar, Mohamed A.A. & Attai, Youssef A. & Mohamed, Mohamed H., 2023. "Shape optimization of a shrouded Archimedean-spiral type wind turbine for small-scale applications," Energy, Elsevier, vol. 263(PB).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:762-:d:760013. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.