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Wind Energy Harvesting and Conversion Systems: A Technical Review

Author

Listed:
  • Sinhara M. H. D. Perera

    (Department of Chemical Engineering, University of Rochester, Rochester, NY 14627, USA)

  • Ghanim Putrus

    (Department of Mathematics, Physics and Electrical Engineering, Northumbria University, Newcastle upon Tyne NE1 8ST, UK)

  • Michael Conlon

    (School of Electrical and Electronic Engineering, Technological University Dublin, D07 ADY7 Dublin, Ireland)

  • Mahinsasa Narayana

    (Department of Chemical and Process Engineering, University of Moratuwa, Moratuwa 10400, Sri Lanka)

  • Keith Sunderland

    (School of Electrical and Electronic Engineering, Technological University Dublin, D07 ADY7 Dublin, Ireland)

Abstract

Wind energy harvesting for electricity generation has a significant role in overcoming the challenges involved with climate change and the energy resource implications involved with population growth and political unrest. Indeed, there has been significant growth in wind energy capacity worldwide with turbine capacity growing significantly over the last two decades. This confidence is echoed in the wind power market and global wind energy statistics. However, wind energy capture and utilisation has always been challenging. Appreciation of the wind as a resource makes for difficulties in modelling and the sensitivities of how the wind resource maps to energy production results in an energy harvesting opportunity. An opportunity that is dependent on different system parameters, namely the wind as a resource, technology and system synergies in realizing an optimal wind energy harvest. This paper presents a thorough review of the state of the art concerning the realization of optimal wind energy harvesting and utilisation. The wind energy resource and, more specifically, the influence of wind speed and wind energy resource forecasting are considered in conjunction with technological considerations and how system optimization can realise more effective operational efficiencies. Moreover, non-technological issues affecting wind energy harvesting are also considered. These include standards and regulatory implications with higher levels of grid integration and higher system non-synchronous penetration (SNSP). The review concludes that hybrid forecasting techniques enable a more accurate and predictable resource appreciation and that a hybrid power system that employs a multi-objective optimization approach is most suitable in achieving an optimal configuration for maximum energy harvesting.

Suggested Citation

  • Sinhara M. H. D. Perera & Ghanim Putrus & Michael Conlon & Mahinsasa Narayana & Keith Sunderland, 2022. "Wind Energy Harvesting and Conversion Systems: A Technical Review," Energies, MDPI, vol. 15(24), pages 1-34, December.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:24:p:9299-:d:997030
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    References listed on IDEAS

    as
    1. Li, Q.S. & Shu, Z.R. & Chen, F.B., 2016. "Performance assessment of tall building-integrated wind turbines for power generation," Applied Energy, Elsevier, vol. 165(C), pages 777-788.
    2. Al-qaness, Mohammed A.A. & Ewees, Ahmed A. & Fan, Hong & Abualigah, Laith & Elaziz, Mohamed Abd, 2022. "Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting," Applied Energy, Elsevier, vol. 314(C).
    3. Thumthae, Chalothorn & Chitsomboon, Tawit, 2009. "Optimal angle of attack for untwisted blade wind turbine," Renewable Energy, Elsevier, vol. 34(5), pages 1279-1284.
    4. Liu, Hui & Chen, Chao, 2019. "Data processing strategies in wind energy forecasting models and applications: A comprehensive review," Applied Energy, Elsevier, vol. 249(C), pages 392-408.
    5. Francisco Haces-Fernandez & Mariee Cruz-Mendoza & Hua Li, 2022. "Onshore Wind Farm Development: Technologies and Layouts," Energies, MDPI, vol. 15(7), pages 1-25, March.
    6. Wang, Jianzhou & Song, Yiliao & Liu, Feng & Hou, Ru, 2016. "Analysis and application of forecasting models in wind power integration: A review of multi-step-ahead wind speed forecasting models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 960-981.
    7. Khanna, Rupali A. & Li, Yanfei & Mhaisalkar, Subodh & Kumar, Mahesh & Liang, Lim Jia, 2019. "Comprehensive energy poverty index: Measuring energy poverty and identifying micro-level solutions in South and Southeast Asia," Energy Policy, Elsevier, vol. 132(C), pages 379-391.
    8. Mohseni, Mansour & Islam, Syed M., 2012. "Review of international grid codes for wind power integration: Diversity, technology and a case for global standard," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3876-3890.
    9. Niveditha, N. & Rajan Singaravel, M.M., 2022. "Optimal sizing of hybrid PV–Wind–Battery storage system for Net Zero Energy Buildings to reduce grid burden," Applied Energy, Elsevier, vol. 324(C).
    10. Kardooni, Roozbeh & Yusoff, Sumiani Binti & Kari, Fatimah Binti, 2016. "Renewable energy technology acceptance in Peninsular Malaysia," Energy Policy, Elsevier, vol. 88(C), pages 1-10.
    11. Wang, Chen & Zhang, Shenghui & Liao, Peng & Fu, Tonglin, 2022. "Wind speed forecasting based on hybrid model with model selection and wind energy conversion," Renewable Energy, Elsevier, vol. 196(C), pages 763-781.
    12. Zhang, Wanqing & Lin, Zi & Liu, Xiaolei, 2022. "Short-term offshore wind power forecasting - A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average (SARIMA), and deep-learning-based Long Short-Te," Renewable Energy, Elsevier, vol. 185(C), pages 611-628.
    13. Narayana, M. & Putrus, G.A. & Jovanovic, M. & Leung, P.S. & McDonald, S., 2012. "Generic maximum power point tracking controller for small-scale wind turbines," Renewable Energy, Elsevier, vol. 44(C), pages 72-79.
    14. Hu, Jianming & Luo, Qingxi & Tang, Jingwei & Heng, Jiani & Deng, Yuwen, 2022. "Conformalized temporal convolutional quantile regression networks for wind power interval forecasting," Energy, Elsevier, vol. 248(C).
    15. Rona, Berk & Güler, Önder, 2015. "Power system integration of wind farms and analysis of grid code requirements," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 100-107.
    16. Morshed, Mohammad Javad & Hmida, Jalel Ben & Fekih, Afef, 2018. "A probabilistic multi-objective approach for power flow optimization in hybrid wind-PV-PEV systems," Applied Energy, Elsevier, vol. 211(C), pages 1136-1149.
    17. Ahshan, R. & Iqbal, M.T. & Mann, George K.I., 2008. "Controller for a small induction-generator based wind-turbine," Applied Energy, Elsevier, vol. 85(4), pages 218-227, April.
    18. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
    19. Gao, Richie & Gao, Zhiwei, 2016. "Pitch control for wind turbine systems using optimization, estimation and compensation," Renewable Energy, Elsevier, vol. 91(C), pages 501-515.
    20. Ekren, Orhan & Ekren, Banu Yetkin, 2008. "Size optimization of a PV/wind hybrid energy conversion system with battery storage using response surface methodology," Applied Energy, Elsevier, vol. 85(11), pages 1086-1101, November.
    21. Fengshuo Bian & Keqilao Meng & Yan Jia & Jianlong Ma & Rihan Hai & Bekir Sahin, 2022. "Application of Effective Wind Speed Estimation and New Sliding Mode Observer in Wind Energy Conversion System," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-11, May.
    22. Gao, Yuyang & Wang, Jianzhou & Yang, Hufang, 2022. "A multi-component hybrid system based on predictability recognition and modified multi-objective optimization for ultra-short-term onshore wind speed forecasting," Renewable Energy, Elsevier, vol. 188(C), pages 384-401.
    23. Wu, Jie & Li, Na & Zhao, Yan & Wang, Jujie, 2022. "Usage of correlation analysis and hypothesis test in optimizing the gated recurrent unit network for wind speed forecasting," Energy, Elsevier, vol. 242(C).
    24. Abdullah, M.A. & Yatim, A.H.M. & Tan, C.W. & Saidur, R., 2012. "A review of maximum power point tracking algorithms for wind energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(5), pages 3220-3227.
    25. Belouda, Malek & Jaafar, Amine & Sareni, Bruno & Roboam, Xavier & Belhadj, Jamel, 2016. "Design methodologies for sizing a battery bank devoted to a stand-alone and electronically passive wind turbine system," Renewable and Sustainable Energy Reviews, Elsevier, vol. 60(C), pages 144-154.
    26. Daphne Schwanz & Math Bollen & Oscar Lennerhag & Anders Larsson, 2021. "Harmonic Transfers for Quantifying Propagation of Harmonics in Wind Power Plants," Energies, MDPI, vol. 14(18), pages 1-27, September.
    27. Baroudi, Jamal A. & Dinavahi, Venkata & Knight, Andrew M., 2007. "A review of power converter topologies for wind generators," Renewable Energy, Elsevier, vol. 32(14), pages 2369-2385.
    28. Til Kristian Vrana & Damian Flynn & Emilio Gomez‐Lazaro & Juha Kiviluoma & Davy Marcel & Nicolaos Cutululis & J. Charles Smith, 2018. "Wind power within European grid codes: Evolution, status and outlook," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 7(3), May.
    29. Al kez, Dlzar & Foley, Aoife M. & McIlwaine, Neil & Morrow, D. John & Hayes, Barry P. & Zehir, M. Alparslan & Mehigan, Laura & Papari, Behnaz & Edrington, Chris S. & Baran, Mesut, 2020. "A critical evaluation of grid stability and codes, energy storage and smart loads in power systems with wind generation," Energy, Elsevier, vol. 205(C).
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