IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v316y2022ics0306261922005104.html
   My bibliography  Save this article

Adaptive neural dynamic surface control for uniform energy exploitation of floating wind turbine

Author

Listed:
  • Keighobadi, Jafar
  • Mohammadian KhalafAnsar, Hadi
  • Naseradinmousavi, Peiman

Abstract

To avoid the harmful effects of global warming on the earth planet and its atmosphere, the expansion of wind energy consumption based on new control techniques leads to improved energy capacity. The development of wind power plants on the sea is more efficient owing to the stronger wind flows in comparison with the onshore structure. Furthermore, stable installation of the offshore wind energy base meets the energy management requirements of today's consumers. As a result, dependency on fossil fuels as the main source of global warming decreases. The feedback control system and on-line sensor signals provide the stability and increased efficiency of the floating wind turbine. The aggressive environment and large size structure of the turbine lead to entering uncertainty in the turbine model and exogenous noises that should not be effectively compensated by conventional control methods. Therefore, a radial based functional neural network (RBFNN) controller is proposed to estimate and compensate the effect of uncertainty on feedback control of the wind turbine. For comparison purposes, a linear quadratic regulator (LQR) state feedback is also developed for optimal control of the floating wind turbines. The neural network adaptively determines the upper bound of uncertainty/noise. Therefore, conservative high-gain control actions to achieve robustness of the classical feedback controllers against structural deviations of the turbine body are decreased. In our newly proposed adaptive control algorithm, using a basic raised cosine function guides to restore the computational efficiency of RBFNN. With the Lyapunov-based stability analysis, the final limits of the closed-loop system and the convergence caused by the terminal sliding mode (TSM) tracking error are determined. Detailed software simulations of both the LQR and the designed RBFNN control systems indicate the superiority of the neural network-based approach.

Suggested Citation

  • Keighobadi, Jafar & Mohammadian KhalafAnsar, Hadi & Naseradinmousavi, Peiman, 2022. "Adaptive neural dynamic surface control for uniform energy exploitation of floating wind turbine," Applied Energy, Elsevier, vol. 316(C).
    • Handle: RePEc:eee:appene:v:316:y:2022:i:c:s0306261922005104
    DOI: 10.1016/j.apenergy.2022.119132
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261922005104
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2022.119132?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. Shi, Zhongtuo & Yao, Wei & Li, Zhouping & Zeng, Lingkang & Zhao, Yifan & Zhang, Runfeng & Tang, Yong & Wen, Jinyu, 2020. "Artificial intelligence techniques for stability analysis and control in smart grids: Methodologies, applications, challenges and future directions," Applied Energy, Elsevier, vol. 278(C).
    2. Waseem Aslam Butt & Lin Yan & Kendrick Amezquita S., 2015. "Adaptive integral dynamic surface control of a hypersonic flight vehicle," International Journal of Systems Science, Taylor & Francis Journals, vol. 46(10), pages 1717-1728, July.
    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. Zhou, Binzhen & Hu, Jianjian & Wang, Yu & Jin, Peng & Jing, Fengmei & Ning, Dezhi, 2023. "Coupled dynamic and power generation characteristics of a hybrid system consisting of a semi-submersible wind turbine and an array of heaving wave energy converters," Renewable Energy, Elsevier, vol. 214(C), pages 23-38.
    2. 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).
    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.

    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. Chao-Chung Hsu & Bi-Hai Jiang & Chun-Cheng Lin, 2023. "A Survey on Recent Applications of Artificial Intelligence and Optimization for Smart Grids in Smart Manufacturing," Energies, MDPI, vol. 16(22), pages 1-15, November.
    2. Chongchong Xu & Zhicheng Liao & Chaojie Li & Xiaojun Zhou & Renyou Xie, 2022. "Review on Interpretable Machine Learning in Smart Grid," Energies, MDPI, vol. 15(12), pages 1-31, June.
    3. Lu, Qing & Zhang, Yufeng, 2022. "A multi-objective optimization model considering users' satisfaction and multi-type demand response in dynamic electricity price," Energy, Elsevier, vol. 240(C).
    4. Michael Meiser & Ingo Zinnikus, 2024. "A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities," Energies, MDPI, vol. 17(9), pages 1-29, April.
    5. Huang, Wanjun & Zhang, Xinran & Zheng, Weiye, 2021. "Resilient power network structure for stable operation of energy systems: A transfer learning approach," Applied Energy, Elsevier, vol. 296(C).
    6. Jose Ulises Castellanos Contreras & Leonardo Rodríguez Urrego, 2023. "Technological Developments in Control Models Using Petri Nets for Smart Grids: A Review," Energies, MDPI, vol. 16(8), pages 1-21, April.
    7. Zhan, Xianwen & Han, Song & Rong, Na & Cao, Yun, 2023. "A hybrid transfer learning method for transient stability prediction considering sample imbalance," Applied Energy, Elsevier, vol. 333(C).
    8. Lutfu Saribulut & Gorkem Ok & Arman Ameen, 2023. "A Case Study on National Electricity Blackout of Turkey," Energies, MDPI, vol. 16(11), pages 1-20, May.
    9. Yin, Linfei & Lu, Yuejiang, 2021. "Expandable deep width learning for voltage control of three-state energy model based smart grids containing flexible energy sources," Energy, Elsevier, vol. 226(C).
    10. Cai, Qingsen & Luo, XingQi & Wang, Peng & Gao, Chunyang & Zhao, Peiyu, 2022. "Hybrid model-driven and data-driven control method based on machine learning algorithm in energy hub and application," Applied Energy, Elsevier, vol. 305(C).
    11. Pranobjyoti Lahon & Aditya Bihar Kandali & Utpal Barman & Ruhit Jyoti Konwar & Debdeep Saha & Manob Jyoti Saikia, 2024. "Deep Neural Network-Based Smart Grid Stability Analysis: Enhancing Grid Resilience and Performance," Energies, MDPI, vol. 17(11), pages 1-17, May.
    12. Walter Leal Filho & Peter Yang & João Henrique Paulino Pires Eustachio & Anabela Marisa Azul & Joshua C. Gellers & Agata Gielczyk & Maria Alzira Pimenta Dinis & Valerija Kozlova, 2023. "Deploying digitalisation and artificial intelligence in sustainable development research," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(6), pages 4957-4988, June.
    13. Fatima Zahra Zahraoui & Mehdi Et-taoussi & Houssam Eddine Chakir & Hamid Ouadi & Brahim Elbhiri, 2023. "Bellman–Genetic Hybrid Algorithm Optimization in Rural Area Microgrids," Energies, MDPI, vol. 16(19), pages 1-26, September.
    14. Hugo Gaspar Hernandez-Palma & Jonny Rafael Plaza Alvarado & Jesús Enrique García Guiliany & Guilherme Luiz Dotto & Claudete Gindri Ramos, 2024. "Implications of Machine Learning in the Generation of Renewable Energies in Latin America from a Globalized Vision: A Systematic Review," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 1-10, March.
    15. Zhang, Xiaojing & Khan, Khalid & Shao, Xuefeng & Oprean-Stan, Camelia & Zhang, Qian, 2024. "The rising role of artificial intelligence in renewable energy development in China," Energy Economics, Elsevier, vol. 132(C).
    16. Prince Waqas Khan & Yongjun Kim & Yung-Cheol Byun & Sang-Joon Lee, 2021. "Influencing Factors Evaluation of Machine Learning-Based Energy Consumption Prediction," Energies, MDPI, vol. 14(21), pages 1-22, November.
    17. Shi, Zhongtuo & Yao, Wei & Zhao, Yifan & Ai, Xiaomeng & Wen, Jinyu & Cheng, Shijie, 2024. "Two-stage weakly supervised learning to mitigate label noise for intelligent identification of power system dominant instability mode," Applied Energy, Elsevier, vol. 359(C).
    18. Noman, Abdullah Al & Tasneem, Zinat & Sahed, Md. Fahad & Muyeen, S.M. & Das, Sajal K. & Alam, Firoz, 2022. "Towards next generation Savonius wind turbine: Artificial intelligence in blade design trends and framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
    19. Nazir, Lubna & Sharifi, Ayyoob, 2024. "An analysis of barriers to the implementation of smart grid technology in Pakistan," Renewable Energy, Elsevier, vol. 220(C).
    20. Musawenkosi Lethumcebo Thanduxolo Zulu & Rudiren Pillay Carpanen & Remy Tiako, 2023. "A Comprehensive Review: Study of Artificial Intelligence Optimization Technique Applications in a Hybrid Microgrid at Times of Fault Outbreaks," Energies, MDPI, vol. 16(4), pages 1-32, February.

    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:appene:v:316:y:2022:i:c:s0306261922005104. 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/405891/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.