IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v45y2015icp336-342.html
   My bibliography  Save this article

Synergies of Wind Turbine control techniques

Author

Listed:
  • Bertašienė, Agnė
  • Azzopardi, Brian

Abstract

During the next decades, the market for Small-Scale Wind Turbines (SSWT) is expected to grow, due to a shift in micro-generation and current trends in distributed energy resources. Meanwhile in the last two decades, there were significant developments in control techniques for Large-Scale Wind Turbines (LSWT). Nonetheless, there exist synergies in Wind Turbine (WT) technologies from small to large scale. The reduction of WTs׳ operation and maintenance costs directly correlate to technical and economic matrices, which is crucial to the success of the wind energy industry. The aim of this paper is to compare WT control techniques from small to large scales levels, identifying common challenges and developments to achieve intelligent control algorithms at the small-to-medium scale levels. Therefore, the potential impact of increasing the competitiveness of wind energy in urban and suburban areas is explored and discussed through affordable and feasible levelised wind electricity costs.

Suggested Citation

  • Bertašienė, Agnė & Azzopardi, Brian, 2015. "Synergies of Wind Turbine control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 336-342.
    • Handle: RePEc:eee:rensus:v:45:y:2015:i:c:p:336-342
    DOI: 10.1016/j.rser.2015.01.063
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032115000738
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.rser.2015.01.063?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. Meyer, David G. & Srinivasan, Sriram & Semrau, Greg, 2013. "Dynamic wind estimation based control for small wind turbines," Renewable Energy, Elsevier, vol. 50(C), pages 259-267.
    2. Saeidi, Davood & Sedaghat, Ahmad & Alamdari, Pourya & Alemrajabi, Ali Akbar, 2013. "Aerodynamic design and economical evaluation of site specific small vertical axis wind turbines," Applied Energy, Elsevier, vol. 101(C), pages 765-775.
    3. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    4. Hirahara, Hiroyuki & Hossain, M. Zakir & Kawahashi, Masaaki & Nonomura, Yoshitami, 2005. "Testing basic performance of a very small wind turbine designed for multi-purposes," Renewable Energy, Elsevier, vol. 30(8), pages 1279-1297.
    5. Mohandes, M. & Rehman, S. & Rahman, S.M., 2011. "Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS)," Applied Energy, Elsevier, vol. 88(11), pages 4024-4032.
    6. Pinar Pérez, Jesús María & García Márquez, Fausto Pedro & Tobias, Andrew & Papaelias, Mayorkinos, 2013. "Wind turbine reliability analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 463-472.
    7. Bououden, S. & Chadli, M. & Filali, S. & El Hajjaji, A., 2012. "Fuzzy model based multivariable predictive control of a variable speed wind turbine: LMI approach," Renewable Energy, Elsevier, vol. 37(1), pages 434-439.
    8. Lubitz, William David, 2014. "Impact of ambient turbulence on performance of a small wind turbine," Renewable Energy, Elsevier, vol. 61(C), pages 69-73.
    9. Adaramola, M.S. & Krogstad, P.-Å., 2011. "Experimental investigation of wake effects on wind turbine performance," Renewable Energy, Elsevier, vol. 36(8), pages 2078-2086.
    10. Arroyo, A. & Manana, M. & Gomez, C. & Fernandez, I. & Delgado, F. & Zobaa, Ahmed F., 2013. "A methodology for the low-cost optimisation of small wind turbine performance," Applied Energy, Elsevier, vol. 104(C), pages 1-9.
    11. Liu, Hui & Tian, Hong-qi & Pan, Di-fu & Li, Yan-fei, 2013. "Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks," Applied Energy, Elsevier, vol. 107(C), pages 191-208.
    12. Audierne, Etienne & Elizondo, Jorge & Bergami, Leonardo & Ibarra, Humberto & Probst, Oliver, 2010. "Analysis of the furling behavior of small wind turbines," Applied Energy, Elsevier, vol. 87(7), pages 2278-2292, July.
    13. Kusiak, Andrew & Zheng, Haiyang, 2010. "Optimization of wind turbine energy and power factor with an evolutionary computation algorithm," Energy, Elsevier, vol. 35(3), pages 1324-1332.
    14. Şerban, I. & Marinescu, C., 2012. "A sensorless control method for variable-speed small wind turbines," Renewable Energy, Elsevier, vol. 43(C), pages 256-266.
    15. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
    16. Islam, M.R. & Mekhilef, S. & Saidur, R., 2013. "Progress and recent trends of wind energy technology," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 456-468.
    17. Fischer, Gunter Reinald & Kipouros, Timoleon & Savill, Anthony Mark, 2014. "Multi-objective optimisation of horizontal axis wind turbine structure and energy production using aerofoil and blade properties as design variables," Renewable Energy, Elsevier, vol. 62(C), pages 506-515.
    18. Maki, Kevin & Sbragio, Ricardo & Vlahopoulos, Nickolas, 2012. "System design of a wind turbine using a multi-level optimization approach," Renewable Energy, Elsevier, vol. 43(C), pages 101-110.
    19. Douak, Fouzi & Melgani, Farid & Benoudjit, Nabil, 2013. "Kernel ridge regression with active learning for wind speed prediction," Applied Energy, Elsevier, vol. 103(C), pages 328-340.
    20. Monfared, Mohammad & Golestan, Saeed, 2012. "Control strategies for single-phase grid integration of small-scale renewable energy sources: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4982-4993.
    21. Bilgili, Mehmet & Sahin, Besir & Yasar, Abdulkadir, 2007. "Application of artificial neural networks for the wind speed prediction of target station using reference stations data," Renewable Energy, Elsevier, vol. 32(14), pages 2350-2360.
    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. Mahela, Om Prakash & Shaik, Abdul Gafoor, 2016. "Comprehensive overview of grid interfaced wind energy generation systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 260-281.
    2. Lim, Chae Wook, 2019. "A demonstration on the similarity of pitch response between MW wind turbine and small-scale simulator," Renewable Energy, Elsevier, vol. 144(C), pages 68-76.

    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. Rocha, P. A. Costa & Rocha, H. H. Barbosa & Carneiro, F. O. Moura & da Silva, M. E. Vieira & de Andrade, C. Freitas, 2016. "A case study on the calibration of the k–ω SST (shear stress transport) turbulence model for small scale wind turbines designed with cambered and symmetrical airfoils," Energy, Elsevier, vol. 97(C), pages 144-150.
    2. Shen, Zhiwei & Ritter, Matthias, 2016. "Forecasting volatility of wind power production," Applied Energy, Elsevier, vol. 176(C), pages 295-308.
    3. Suganthi, L. & Iniyan, S. & Samuel, Anand A., 2015. "Applications of fuzzy logic in renewable energy systems – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 585-607.
    4. Astolfi, Davide & Castellani, Francesco & Garinei, Alberto & Terzi, Ludovico, 2015. "Data mining techniques for performance analysis of onshore wind farms," Applied Energy, Elsevier, vol. 148(C), pages 220-233.
    5. Emejeamara, F.C. & Tomlin, A.S., 2020. "A method for estimating the potential power available to building mounted wind turbines within turbulent urban air flows," Renewable Energy, Elsevier, vol. 153(C), pages 787-800.
    6. Xiu, Chunbo & Wang, Tiantian & Tian, Meng & Li, Yanqing & Cheng, Yi, 2014. "Short-term prediction method of wind speed series based on fractal interpolation," Chaos, Solitons & Fractals, Elsevier, vol. 68(C), pages 89-97.
    7. Iqbal, M. & Azam, M. & Naeem, M. & Khwaja, A.S. & Anpalagan, A., 2014. "Optimization classification, algorithms and tools for renewable energy: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 39(C), pages 640-654.
    8. 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.
    9. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    10. Ruiz de la Hermosa González-Carrato, Raúl & García Márquez, Fausto Pedro & Dimlaye, Vichaar, 2015. "Maintenance management of wind turbines structures via MFCs and wavelet transforms," Renewable and Sustainable Energy Reviews, Elsevier, vol. 48(C), pages 472-482.
    11. Pagnini, Luisa C. & Burlando, Massimiliano & Repetto, Maria Pia, 2015. "Experimental power curve of small-size wind turbines in turbulent urban environment," Applied Energy, Elsevier, vol. 154(C), pages 112-121.
    12. Marwa Hassan & Alsnosy Balbaa & Hanady H. Issa & Noha H. El-Amary, 2018. "Asymptotic Output Tracked Artificial Immunity Controller for Eco-Maximum Power Point Tracking of Wind Turbine Driven by Doubly Fed Induction Generator," Energies, MDPI, vol. 11(10), pages 1-25, October.
    13. Ma, Jinrui & Fouladirad, Mitra & Grall, Antoine, 2018. "Flexible wind speed generation model: Markov chain with an embedded diffusion process," Energy, Elsevier, vol. 164(C), pages 316-328.
    14. Xiaobing Kong & Lele Ma & Xiangjie Liu & Mohamed Abdelkarim Abdelbaky & Qian Wu, 2020. "Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions," Energies, MDPI, vol. 13(1), pages 1-21, January.
    15. Anicic, Obrad & Jovic, Srdjan, 2016. "Adaptive neuro-fuzzy approach for ducted tidal turbine performance estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1111-1116.
    16. Ana Rita Nunes & Hugo Morais & Alberto Sardinha, 2021. "Use of Learning Mechanisms to Improve the Condition Monitoring of Wind Turbine Generators: A Review," Energies, MDPI, vol. 14(21), pages 1-22, November.
    17. Tugce Demirdelen & Pırıl Tekin & Inayet Ozge Aksu & Firat Ekinci, 2019. "The Prediction Model of Characteristics for Wind Turbines Based on Meteorological Properties Using Neural Network Swarm Intelligence," Sustainability, MDPI, vol. 11(17), pages 1-18, September.
    18. Jia, Liangyue & Hao, Jia & Hall, John & Nejadkhaki, Hamid Khakpour & Wang, Guoxin & Yan, Yan & Sun, Mengyuan, 2021. "A reinforcement learning based blade twist angle distribution searching method for optimizing wind turbine energy power," Energy, Elsevier, vol. 215(PA).
    19. Veisi, Amin Allah & Shafiei Mayam, Mohammad Hossein, 2017. "Effects of blade rotation direction in the wake region of two in-line turbines using Large Eddy Simulation," Applied Energy, Elsevier, vol. 197(C), pages 375-392.
    20. Chi-Jeng Bai & Wei-Cheng Wang & Po-Wei Chen & Wen-Tong Chong, 2014. "System Integration of the Horizontal-Axis Wind Turbine: The Design of Turbine Blades with an Axial-Flux Permanent Magnet Generator," Energies, MDPI, vol. 7(11), pages 1-21, November.

    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:45:y:2015:i:c:p:336-342. 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.