IDEAS home Printed from https://ideas.repec.org/a/eee/rensus/v151y2021ics1364032121008030.html
   My bibliography  Save this article

A synthesis of feasible control methods for floating offshore wind turbine system dynamics

Author

Listed:
  • Shah, Kamran Ali
  • Meng, Fantai
  • Li, Ye
  • Nagamune, Ryozo
  • Zhou, Yarong
  • Ren, Zhengru
  • Jiang, Zhiyu

Abstract

During the past decade, the development of offshore wind energy has transitioned from near shore with shallow water to offshore middle-depth water regions. Consequently, the energy conversion technology has shifted from bottom-fixed wind turbines to floating offshore wind turbines. Floating offshore wind turbines are considered more suitable, but their cost is still very high. One of the main reasons for this is that the system dynamics control method is not well-adapted, thereby affecting the performance and reliability of the wind turbine system. The additional motion of the platform tends to compromise the system’s performance in terms of power maximization, power regulation, and load mitigation. To provide a recommendation based on the advantages and disadvantages of different control methods, we systematically analyze feasible control methods for existing floating offshore wind turbine designs. Based on a brief overview of floating offshore wind turbine system dynamics, we present several promising control methods by classifying them as blade-pitch-based and mass–spring–damper-based. Furthermore, we emphasize on the incoming wind and wave forecasting associated with the control methods. We then compare different methods by evaluating a matrix involving platform motion minimization, load mitigation, and power regulation and identify the advantages and disadvantages. Finally, recommendations and suggestions for further research are provided by integrating the advantageous control algorithm and forecasting technologies to reduce costs.

Suggested Citation

  • Shah, Kamran Ali & Meng, Fantai & Li, Ye & Nagamune, Ryozo & Zhou, Yarong & Ren, Zhengru & Jiang, Zhiyu, 2021. "A synthesis of feasible control methods for floating offshore wind turbine system dynamics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  • Handle: RePEc:eee:rensus:v:151:y:2021:i:c:s1364032121008030
    DOI: 10.1016/j.rser.2021.111525
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1364032121008030
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.rser.2021.111525?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. Guo, Zhenhai & Zhao, Weigang & Lu, Haiyan & Wang, Jianzhou, 2012. "Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model," Renewable Energy, Elsevier, vol. 37(1), pages 241-249.
    2. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    3. Gneiting, Tilmann & Larson, Kristin & Westrick, Kenneth & Genton, Marc G. & Aldrich, Eric, 2006. "Calibrated Probabilistic Forecasting at the Stateline Wind Energy Center: The Regime-Switching SpaceTime Method," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 968-979, September.
    4. Borg, Michael & Collu, Maurizio, 2015. "Offshore floating vertical axis wind turbines, dynamics modelling state of the art. Part III: Hydrodynamics and coupled modelling approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 46(C), pages 296-310.
    5. Ma, Yu & Sclavounos, Paul D. & Cross-Whiter, John & Arora, Dhiraj, 2018. "Wave forecast and its application to the optimal control of offshore floating wind turbine for load mitigation," Renewable Energy, Elsevier, vol. 128(PA), pages 163-176.
    6. Wu, Xiaoni & Hu, Yu & Li, Ye & Yang, Jian & Duan, Lei & Wang, Tongguang & Adcock, Thomas & Jiang, Zhiyu & Gao, Zhen & Lin, Zhiliang & Borthwick, Alistair & Liao, Shijun, 2019. "Foundations of offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 104(C), pages 379-393.
    7. Arash E. Samani & Jeroen D. M. De Kooning & Nezmin Kayedpour & Narender Singh & Lieven Vandevelde, 2020. "The Impact of Pitch-To-Stall and Pitch-To-Feather Control on the Structural Loads and the Pitch Mechanism of a Wind Turbine," Energies, MDPI, vol. 13(17), pages 1-21, September.
    8. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    9. Li, Gong & Shi, Jing, 2012. "Applications of Bayesian methods in wind energy conversion systems," Renewable Energy, Elsevier, vol. 43(C), pages 1-8.
    10. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang & Zhang, Yixin, 2015. "A self-adaptive hybrid approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 78(C), pages 374-385.
    11. Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
    12. Sfetsos, A., 2002. "A novel approach for the forecasting of mean hourly wind speed time series," Renewable Energy, Elsevier, vol. 27(2), pages 163-174.
    13. Mohandes, M.A. & Halawani, T.O. & Rehman, S. & Hussain, Ahmed A., 2004. "Support vector machines for wind speed prediction," Renewable Energy, Elsevier, vol. 29(6), pages 939-947.
    14. Ait Maatallah, Othman & Achuthan, Ajit & Janoyan, Kerop & Marzocca, Pier, 2015. "Recursive wind speed forecasting based on Hammerstein Auto-Regressive model," Applied Energy, Elsevier, vol. 145(C), pages 191-197.
    15. Dai, Kaoshan & Bergot, Anthony & Liang, Chao & Xiang, Wei-Ning & Huang, Zhenhua, 2015. "Environmental issues associated with wind energy – A review," Renewable Energy, Elsevier, vol. 75(C), pages 911-921.
    16. Wang, Jianzhou & Qin, Shanshan & Jin, Shiqiang & Wu, Jie, 2015. "Estimation methods review and analysis of offshore extreme wind speeds and wind energy resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 42(C), pages 26-42.
    17. Ren, Zhengru & Verma, Amrit Shankar & Li, Ye & Teuwen, Julie J.E. & Jiang, Zhiyu, 2021. "Offshore wind turbine operations and maintenance: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    18. Kaldellis, J.K. & Kapsali, M., 2013. "Shifting towards offshore wind energy—Recent activity and future development," Energy Policy, Elsevier, vol. 53(C), pages 136-148.
    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. Papi, F. & Bianchini, A., 2022. "Technical challenges in floating offshore wind turbine upscaling: A critical analysis based on the NREL 5 MW and IEA 15 MW Reference Turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
    2. Ferri, Giulio & Marino, Enzo, 2023. "Site-specific optimizations of a 10 MW floating offshore wind turbine for the Mediterranean Sea," Renewable Energy, Elsevier, vol. 202(C), pages 921-941.
    3. Truong, Hoai Vu Anh & Dang, Tri Dung & Vo, Cong Phat & Ahn, Kyoung Kwan, 2022. "Active control strategies for system enhancement and load mitigation of floating offshore wind turbines: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    4. Xiaoxun, Zhu & Xinyu, Hang & Xiaoxia, Gao & Xing, Yang & Zixu, Xu & Yu, Wang & Huaxin, Liu, 2022. "Research on crack detection method of wind turbine blade based on a deep learning method," Applied Energy, Elsevier, vol. 328(C).
    5. Mohammad Barooni & Turaj Ashuri & Deniz Velioglu Sogut & Stephen Wood & Shiva Ghaderpour Taleghani, 2022. "Floating Offshore Wind Turbines: Current Status and Future Prospects," Energies, MDPI, vol. 16(1), pages 1-28, December.
    6. Pustina, L. & Serafini, J. & Pasquali, C. & Solero, L. & Lidozzi, A. & Gennaretti, M., 2023. "A novel resonant controller for sea-induced rotor blade vibratory loads reduction on floating offshore wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    7. Grant, Elenya & Johnson, Kathryn & Damiani, Rick & Phadnis, Mandar & Pao, Lucy, 2023. "Buoyancy can ballast control for increased power generation of a floating offshore wind turbine with a light-weight semi-submersible platform," Applied Energy, Elsevier, vol. 330(PB).
    8. Meng, Fantai & Sergiienko, Nataliia & Ding, Boyin & Zhou, Binzhen & Silva, Leandro Souza Pinheiro Da & Cazzolato, Benjamin & Li, Ye, 2023. "Co-located offshore wind–wave energy systems: Can motion suppression and reliable power generation be achieved simultaneously?," Applied Energy, Elsevier, vol. 331(C).
    9. Zhang, Lijun & Li, Ye & Xu, Wenhao & Gao, Zhiteng & Fang, Long & Li, Rongfu & Ding, Boyin & Zhao, Bin & Leng, Jun & He, Fenglan, 2022. "Systematic analysis of performance and cost of two floating offshore wind turbines with significant interactions," Applied Energy, Elsevier, vol. 321(C).
    10. Cezary Banaszak & Andrzej Gawlik & Paweł Szcześniak & Marcin Rabe & Katarzyna Widera & Yuriy Bilan & Agnieszka Łopatka & Ewelina Gutowska, 2023. "Economic and Energy Analysis of the Construction of a Wind Farm with Infrastructure in the Baltic Sea," Energies, MDPI, vol. 16(16), pages 1-20, August.
    11. Chen, Lingte & Yang, Jin & Lou, Chengwei, 2024. "Characterizing ramp events in floating offshore wind power through a fully coupled electrical-mechanical mathematical model," Renewable Energy, Elsevier, vol. 221(C).
    12. Zheng, Yidan & Liu, Huiwen & Chamorro, Leonardo P. & Zhao, Zhenzhou & Li, Ye & Zheng, Yuan & Tang, Kexin, 2023. "Impact of turbulence level on intermittent-like events in the wake of a model wind turbine," Renewable Energy, Elsevier, vol. 203(C), pages 45-55.
    13. Arabgolarcheh, Alireza & Rouhollahi, Amirhossein & Benini, Ernesto, 2023. "Analysis of middle-to-far wake behind floating offshore wind turbines in the presence of multiple platform motions," Renewable Energy, Elsevier, vol. 208(C), pages 546-560.
    14. 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).
    15. Subbulakshmi, A. & Verma, Mohit & Keerthana, M. & Sasmal, Saptarshi & Harikrishna, P. & Kapuria, Santosh, 2022. "Recent advances in experimental and numerical methods for dynamic analysis of floating offshore wind turbines — An integrated review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 164(C).
    16. Hyeonjeong Ahn & Yoon-Jin Ha & Kyong-Hwan Kim, 2023. "Load Evaluation for Tower Design of Large Floating Offshore Wind Turbine System According to Wave Conditions," Energies, MDPI, vol. 16(4), pages 1-18, February.
    17. Qu, Yang & Swales, J. Kim & Hooper, Tara & Austen, Melanie C. & Wang, Xinhao & Papathanasopoulou, Eleni & Huang, Junling & Yan, Xiaoyu, 2023. "Economic trade-offs in marine resource use between offshore wind farms and fisheries in Scottish waters," Energy Economics, Elsevier, vol. 125(C).
    18. Emilio García & Antonio Correcher & Eduardo Quiles & Fernando Tamarit & Francisco Morant, 2022. "Control and Supervision Requirements for Floating Hybrid Generator Systems," IJERPH, MDPI, vol. 19(19), pages 1-22, October.
    19. Gao, Qiang & Bechlenberg, Alva & Jayawardhana, Bayu & Ertugrul, Nesimi & Vakis, Antonis I. & Ding, Boyin, 2024. "Techno-economic assessment of offshore wind and hybrid wind–wave farms with energy storage systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    20. Shi Liu & Yi Yang & Chengyuan Wang & Yuangang Tu & Zhenqing Liu, 2021. "Proposal of a Novel Mooring System Using Three-Bifurcated Mooring Lines for Spar-Type Off-Shore Wind Turbines," Energies, MDPI, vol. 14(24), pages 1-33, December.

    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. Jiang, Ping & Wang, Yun & Wang, Jianzhou, 2017. "Short-term wind speed forecasting using a hybrid model," Energy, Elsevier, vol. 119(C), pages 561-577.
    2. Tascikaraoglu, A. & Uzunoglu, M., 2014. "A review of combined approaches for prediction of short-term wind speed and power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 243-254.
    3. Flavie Didier & Yong-Chao Liu & Salah Laghrouche & Daniel Depernet, 2024. "A Comprehensive Review on Advanced Control Methods for Floating Offshore Wind Turbine Systems above the Rated Wind Speed," Energies, MDPI, vol. 17(10), pages 1-33, May.
    4. He, Qingqing & Wang, Jianzhou & Lu, Haiyan, 2018. "A hybrid system for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 226(C), pages 756-771.
    5. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
    6. Erasmo Cadenas & Wilfrido Rivera & Rafael Campos-Amezcua & Christopher Heard, 2016. "Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model," Energies, MDPI, vol. 9(2), pages 1-15, February.
    7. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
    8. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    9. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    10. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.
    11. M. K. Islam & N. M. S. Hassan & M. G. Rasul & Kianoush Emami & Ashfaque Ahmed Chowdhury, 2023. "Forecasting of Solar and Wind Resources for Power Generation," Energies, MDPI, vol. 16(17), pages 1-23, August.
    12. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    13. Liu, Hui & Chen, Chao & Tian, Hong-qi & Li, Yan-fei, 2012. "A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks," Renewable Energy, Elsevier, vol. 48(C), pages 545-556.
    14. Erdong Zhao & Jing Zhao & Liwei Liu & Zhongyue Su & Ning An, 2015. "Hybrid Wind Speed Prediction Based on a Self-Adaptive ARIMAX Model with an Exogenous WRF Simulation," Energies, MDPI, vol. 9(1), pages 1-20, December.
    15. Emeksiz, Cem & Tan, Mustafa, 2022. "Multi-step wind speed forecasting and Hurst analysis using novel hybrid secondary decomposition approach," Energy, Elsevier, vol. 238(PA).
    16. Xuejiao Ma & Dandan Liu, 2016. "Comparative Study of Hybrid Models Based on a Series of Optimization Algorithms and Their Application in Energy System Forecasting," Energies, MDPI, vol. 9(8), pages 1-34, August.
    17. Zhao, Weigang & Wei, Yi-Ming & Su, Zhongyue, 2016. "One day ahead wind speed forecasting: A resampling-based approach," Applied Energy, Elsevier, vol. 178(C), pages 886-901.
    18. Liu, Hui & Tian, Hong-qi & Liang, Xi-feng & Li, Yan-fei, 2015. "Wind speed forecasting approach using secondary decomposition algorithm and Elman neural networks," Applied Energy, Elsevier, vol. 157(C), pages 183-194.
    19. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    20. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.

    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:151:y:2021:i:c:s1364032121008030. 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.