IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v51y2015icp1023-1037.html
   My bibliography  Save this article

A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm

Author

Listed:
  • Assareh, Ehsanolah
  • Biglari, Mojtaba

Abstract

This paper presents a hybrid method for generator torque control in wind turbines. The generator torque control is usually used in lower wind speeds in order to capture the maximum power. In the proposed method, the wind turbine generator torque is regulated using a proportional and integral (PI) controller. In order to tune the PI gains, a radial basis function (RBF) neural network is used. The optimal dataset to train this neural network is provided by the Gravitational Search Algorithm (GSA). A 5MW wind turbine model based on FAST (Fatigue, Aero-dynamics, Structures and Turbulence) software code developed at the US National Renewable Energy Laboratory (NREL) is used to simulate and verify the results. The simulation results show that the proposed method has a good performance.

Suggested Citation

  • Assareh, Ehsanolah & Biglari, Mojtaba, 2015. "A novel approach to capture the maximum power from variable speed wind turbines using PI controller, RBF neural network and GSA evolutionary algorithm," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1023-1037.
    • Handle: RePEc:eee:rensus:v:51:y:2015:i:c:p:1023-1037
    DOI: 10.1016/j.rser.2015.07.034
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032115006814
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2015.07.034?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jena, Debashisha & Rajendran, Saravanakumar, 2015. "A review of estimation of effective wind speed based control of wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 1046-1062.
    2. Mostafaeipour, Ali, 2010. "Feasibility study of offshore wind turbine installation in Iran compared with the world," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(7), pages 1722-1743, September.
    3. Hassan, H.M. & ElShafei, A.L. & Farag, W.A. & Saad, M.S., 2012. "A robust LMI-based pitch controller for large wind turbines," Renewable Energy, Elsevier, vol. 44(C), pages 63-71.
    4. Mérida, Jován & Aguilar, Luis T. & Dávila, Jorge, 2014. "Analysis and synthesis of sliding mode control for large scale variable speed wind turbine for power optimization," Renewable Energy, Elsevier, vol. 71(C), pages 715-728.
    5. Belmokhtar, K. & Doumbia, M.L. & Agbossou, K., 2014. "Novel fuzzy logic based sensorless maximum power point tracking strategy for wind turbine systems driven DFIG (doubly-fed induction generator)," Energy, Elsevier, vol. 76(C), pages 679-693.
    6. Mostafaeipour, Ali, 2010. "Feasibility study of harnessing wind energy for turbine installation in province of Yazd in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 14(1), pages 93-111, January.
    7. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    8. Oh, Ki-Yong & Park, Joon-Young & Lee, Jun-Shin & Lee, JaeKyung, 2015. "Implementation of a torque and a collective pitch controller in a wind turbine simulator to characterize the dynamics at three control regions," Renewable Energy, Elsevier, vol. 79(C), pages 150-160.
    9. Mohamed, Amal Z. & Eskander, Mona N. & Ghali, Fadia A., 2001. "Fuzzy logic control based maximum power tracking of a wind energy system," Renewable Energy, Elsevier, vol. 23(2), pages 235-245.
    10. Kortabarria, Iñigo & Andreu, Jon & Martínez de Alegría, Iñigo & Jiménez, Jaime & Gárate, José Ignacio & Robles, Eider, 2014. "A novel adaptative maximum power point tracking algorithm for small wind turbines," Renewable Energy, Elsevier, vol. 63(C), pages 785-796.
    11. Chehouri, Adam & Younes, Rafic & Ilinca, Adrian & Perron, Jean, 2015. "Review of performance optimization techniques applied to wind turbines," Applied Energy, Elsevier, vol. 142(C), pages 361-388.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Tiwari, Ramji & Babu, N. Ramesh, 2016. "Recent developments of control strategies for wind energy conversion system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 268-285.
    2. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    3. M. A. Hannan & Ali Q. Al-Shetwi & M. S. Mollik & Pin Jern Ker & M. Mannan & M. Mansor & Hussein M. K. Al-Masri & T. M. Indra Mahlia, 2023. "Wind Energy Conversions, Controls, and Applications: A Review for Sustainable Technologies and Directions," Sustainability, MDPI, vol. 15(5), pages 1-30, February.
    4. Xin Wu & Yanhe Xu & Jie Liu & Cong Lv & Jianzhong Zhou & Qing Zhang, 2019. "Characteristics Analysis and Fuzzy Fractional-Order PID Parameter Optimization for Primary Frequency Modulation of a Pumped Storage Unit Based on a Multi-Objective Gravitational Search Algorithm," Energies, MDPI, vol. 13(1), pages 1-20, December.
    5. Marugán, Alberto Pliego & Márquez, Fausto Pedro García & Perez, Jesus María Pinar & Ruiz-Hernández, Diego, 2018. "A survey of artificial neural network in wind energy systems," Applied Energy, Elsevier, vol. 228(C), pages 1822-1836.
    6. Sergio Fragoso & Juan Garrido & Francisco Vázquez & Fernando Morilla, 2017. "Comparative Analysis of Decoupling Control Methodologies and H ∞ Multivariable Robust Control for Variable-Speed, Variable-Pitch Wind Turbines: Application to a Lab-Scale Wind Turbine," Sustainability, MDPI, vol. 9(5), pages 1-21, April.
    7. Md Rasel Sarkar & Sabariah Julai & Chong Wen Tong & Moslem Uddin & M.F. Romlie & GM Shafiullah, 2020. "Hybrid Pitch Angle Controller Approaches for Stable Wind Turbine Power under Variable Wind Speed," Energies, MDPI, vol. 13(14), pages 1-19, July.
    8. Ramji Tiwari & Sanjeevikumar Padmanaban & Ramesh Babu Neelakandan, 2017. "Coordinated Control Strategies for a Permanent Magnet Synchronous Generator Based Wind Energy Conversion System," Energies, MDPI, vol. 10(10), pages 1-17, September.
    9. Han, Yongming & Du, Zilan & Hu, Xuan & Li, Yeqing & Cai, Di & Fan, Jinzhen & Geng, Zhiqiang, 2023. "Production prediction modeling of food waste anaerobic digestion for resources saving based on SMOTE-LSTM," Applied Energy, Elsevier, vol. 352(C).

    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. Kumar, Dipesh & Chatterjee, Kalyan, 2016. "A review of conventional and advanced MPPT algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 55(C), pages 957-970.
    2. Fathabadi, Hassan, 2016. "Novel high-efficient unified maximum power point tracking controller for hybrid fuel cell/wind systems," Applied Energy, Elsevier, vol. 183(C), pages 1498-1510.
    3. Tiwari, Ramji & Babu, N. Ramesh, 2016. "Recent developments of control strategies for wind energy conversion system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 66(C), pages 268-285.
    4. Ram, J.Prasanth & Rajasekar, N. & Miyatake, Masafumi, 2017. "Design and overview of maximum power point tracking techniques in wind and solar photovoltaic systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1138-1159.
    5. Maheshwari, Zeel & Kengne, Kamgang & Bhat, Omkar, 2023. "A comprehensive review on wind turbine emulators," Renewable and Sustainable Energy Reviews, Elsevier, vol. 180(C).
    6. Fathabadi, Hassan, 2016. "Maximum mechanical power extraction from wind turbines using novel proposed high accuracy single-sensor-based maximum power point tracking technique," Energy, Elsevier, vol. 113(C), pages 1219-1230.
    7. Fazelpour, Farivar & Markarian, Elin & Soltani, Nima, 2017. "Wind energy potential and economic assessment of four locations in Sistan and Balouchestan province in Iran," Renewable Energy, Elsevier, vol. 109(C), pages 646-667.
    8. Fazelpour, Farivar & Soltani, Nima & Soltani, Sina & Rosen, Marc A., 2015. "Assessment of wind energy potential and economics in the north-western Iranian cities of Tabriz and Ardabil," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 87-99.
    9. Mollahosseini, Arash & Hosseini, Seyed Amid & Jabbari, Mostafa & Figoli, Alberto & Rahimpour, Ahmad, 2017. "Renewable energy management and market in Iran: A holistic review on current state and future demands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 80(C), pages 774-788.
    10. Bahaj, AbuBakr S. & Mahdy, Mostafa & Alghamdi, Abdulsalam S. & Richards, David J., 2020. "New approach to determine the Importance Index for developing offshore wind energy potential sites: Supported by UK and Arabian Peninsula case studies," Renewable Energy, Elsevier, vol. 152(C), pages 441-457.
    11. Al-Nassar, W.K. & Neelamani, S. & Al-Salem, K.A. & Al-Dashti, H.A., 2019. "Feasibility of offshore wind energy as an alternative source for the state of Kuwait," Energy, Elsevier, vol. 169(C), pages 783-796.
    12. Islam, A.B.M. Saiful & Jameel, Mohammed & Jumaat, Mohd Zamin & Shirazi, S.M. & Salman, Firas A., 2012. "Review of offshore energy in Malaysia and floating Spar platform for sustainable exploration," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(8), pages 6268-6284.
    13. Karabacak, Murat, 2019. "A new perturb and observe based higher order sliding mode MPPT control of wind turbines eliminating the rotor inertial effect," Renewable Energy, Elsevier, vol. 133(C), pages 807-827.
    14. Saeidi, D. & Mirhosseini, M. & Sedaghat, A. & Mostafaeipour, A., 2011. "Feasibility study of wind energy potential in two provinces of Iran: North and South Khorasan," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3558-3569.
    15. 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.
    16. Mostafaeipour, A. & Sedaghat, A. & Dehghan-Niri, A.A. & Kalantar, V., 2011. "Wind energy feasibility study for city of Shahrbabak in Iran," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(6), pages 2545-2556, August.
    17. Alkhalidi, Mohamad A. & Al-Dabbous, Shoug Kh. & Neelamani, S. & Aldashti, Hassan A., 2019. "Wind energy potential at coastal and offshore locations in the state of Kuwait," Renewable Energy, Elsevier, vol. 135(C), pages 529-539.
    18. Song, Dongran & Yang, Jian & Su, Mei & Liu, Anfeng & Cai, Zili & Liu, Yao & Joo, Young Hoon, 2017. "A novel wind speed estimator-integrated pitch control method for wind turbines with global-power regulation," Energy, Elsevier, vol. 138(C), pages 816-830.
    19. Sun, Peng & Li, Jian & Wang, Caisheng & Lei, Xiao, 2016. "A generalized model for wind turbine anomaly identification based on SCADA data," Applied Energy, Elsevier, vol. 168(C), pages 550-567.
    20. Mostafaeipour, Ali & Sedaghat, Ahmad & Ghalishooyan, Morteza & Dinpashoh, Yagob & Mirhosseini, Mojtaba & Sefid, Mohammad & Pour-Rezaei, Maryam, 2013. "Evaluation of wind energy potential as a power generation source for electricity production in Binalood, Iran," Renewable Energy, Elsevier, vol. 52(C), pages 222-229.

    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:eee:rensus:v:51:y:2015:i:c:p:1023-1037. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/600126/description#description .

    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.