IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i1p184-d304011.html
   My bibliography  Save this article

Wind Turbine Control Using Nonlinear Economic Model Predictive Control over All Operating Regions

Author

Listed:
  • Xiaobing Kong

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Lele Ma

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Xiangjie Liu

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

  • Mohamed Abdelkarim Abdelbaky

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China
    Electrical Power and Machines Engineering Department, Faculty of Engineering, Cairo University, Giza 12613, Egypt)

  • Qian Wu

    (The State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China)

Abstract

With the gradual increase in the installed capacity of wind turbines, more and more attention has been paid to the economy of wind power. Economic model-predictive control (EMPC) has been developed as an effective advanced control strategy, which can improve the dynamic economy performance of the system. However, the variable-speed wind turbine (VSWT) system widely used is generally nonlinear and highly coupled nonaffine systems, containing multiple economic terms. Therefore, a nonlinear EMPC strategy considering power maximization and mechanical load minimization is proposed based on the comprehensive VSWT model, including the dynamics of the tower and the gearbox in this paper. Three groups of simulations verify the effectiveness and reliability/practicability of the proposed nonlinear EMPC strategy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:1:p:184-:d:304011
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/1/184/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/1/184/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Kusiak, Andrew & Zhang, Zijun & Verma, Anoop, 2013. "Prediction, operations, and condition monitoring in wind energy," Energy, Elsevier, vol. 60(C), pages 1-12.
    2. Wakui, Tetsuya & Yoshimura, Motoki & Yokoyama, Ryohei, 2017. "Multiple-feedback control of power output and platform pitching motion for a floating offshore wind turbine-generator system," Energy, Elsevier, vol. 141(C), pages 563-578.
    3. Moodi, Hoda & Bustan, Danyal, 2019. "Wind turbine control using T-S systems with nonlinear consequent parts," Energy, Elsevier, vol. 172(C), pages 922-931.
    4. 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.
    5. Raja M. Imran & D. M. Akbar Hussain & Bhawani Shanker Chowdhry, 2018. "Parameterized Disturbance Observer Based Controller to Reduce Cyclic Loads of Wind Turbine," Energies, MDPI, vol. 11(5), pages 1-13, May.
    6. Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Jiang, Di, 2020. "Design and implementation of partial offline fuzzy model-predictive pitch controller for large-scale wind-turbines," Renewable Energy, Elsevier, vol. 145(C), pages 981-996.
    7. Song, Dongran & Yang, Jian & Dong, Mi & Joo, Young Hoon, 2017. "Model predictive control with finite control set for variable-speed wind turbines," Energy, Elsevier, vol. 126(C), pages 564-572.
    8. Hur, Sung-ho, 2018. "Modelling and control of a wind turbine and farm," Energy, Elsevier, vol. 156(C), pages 360-370.
    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. Xiangjie Liu & Le Feng & Xiaobing Kong, 2022. "A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System," Energies, MDPI, vol. 15(13), pages 1-22, June.
    2. Song, Dongran & Tu, Yanping & Wang, Lei & Jin, Fangjun & Li, Ziqun & Huang, Chaoneng & Xia, E & Rizk-Allah, Rizk M. & Yang, Jian & Su, Mei & Hoon Joo, Young, 2022. "Coordinated optimization on energy capture and torque fluctuation of wind turbines via variable weight NMPC with fuzzy regulator," Applied Energy, Elsevier, vol. 312(C).
    3. 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.
    4. Teng, Yiming & Hu, Dewen & Wu, Feng & Zhang, Ridong & Gao, Furong, 2020. "Fast economic model predictive control for marine current turbine generator system," Renewable Energy, Elsevier, vol. 166(C), pages 108-116.
    5. Bilal Naji Alhasnawi & Basil H. Jasim & Walid Issa & Amjad Anvari-Moghaddam & Frede Blaabjerg, 2020. "A New Robust Control Strategy for Parallel Operated Inverters in Green Energy Applications," Energies, MDPI, vol. 13(13), pages 1-31, July.
    6. José Antonio Cortajarena & Oscar Barambones & Patxi Alkorta & Jon Cortajarena, 2021. "Grid Frequency and Amplitude Control Using DFIG Wind Turbines in a Smart Grid," Mathematics, MDPI, vol. 9(2), pages 1-18, January.
    7. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
    8. Pustina, L. & Biral, F. & Serafini, J., 2022. "A novel Economic Nonlinear Model Predictive Controller for power maximisation on wind turbines," Renewable and Sustainable Energy Reviews, Elsevier, vol. 170(C).
    9. Francesco Castellani & Davide Astolfi, 2020. "Editorial on Special Issue “Wind Turbine Power Optimization Technology”," Energies, MDPI, vol. 13(7), pages 1-4, April.
    10. Abhinandan Routray & Yiza Srikanth Reddy & Sung-ho Hur, 2023. "Predictive Control of a Wind Turbine Based on Neural Network-Based Wind Speed Estimation," Sustainability, MDPI, vol. 15(12), pages 1-22, June.
    11. Jie Bao & Hong Yue, 2022. "Design and Assessment of a LIDAR-Based Model Predictive Wind Turbine Control," Energies, MDPI, vol. 15(17), pages 1-19, September.
    12. Abdoos, Ali Akbar & Abdoos, Hatef & Kazemitabar, Javad & Mobashsher, Mohammad Mehdi & Khaloo, Hooman, 2023. "An intelligent hybrid method based on Monte Carlo simulation for short-term probabilistic wind power prediction," Energy, Elsevier, vol. 278(PA).
    13. Kong, Xiaobing & Ma, Lele & Wang, Ce & Guo, Shifan & Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Lee, Kwang Y., 2022. "Large-scale wind farm control using distributed economic model predictive scheme," Renewable Energy, Elsevier, vol. 181(C), pages 581-591.
    14. Stefano Dettori & Alessandro Maddaloni & Filippo Galli & Valentina Colla & Federico Bucciarelli & Damaso Checcacci & Annamaria Signorini, 2021. "Steam Turbine Rotor Stress Control through Nonlinear Model Predictive Control," Energies, MDPI, vol. 14(13), pages 1-30, July.
    15. Atsushi Yamaguchi & Iman Yousefi & Takeshi Ishihara, 2020. "Reduction in the Fluctuating Load on Wind Turbines by Using a Combined Nacelle Acceleration Feedback and Lidar-Based Feedforward Control," Energies, MDPI, vol. 13(17), pages 1-18, September.

    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. Song, Dongran & Yang, Yinggang & Zheng, Songyue & Tang, Weiyi & Yang, Jian & Su, Mei & Yang, Xuebing & Joo, Young Hoon, 2019. "Capacity factor estimation of variable-speed wind turbines considering the coupled influence of the QN-curve and the air density," Energy, Elsevier, vol. 183(C), pages 1049-1060.
    2. Gianluca Pepe & Federica Mezzani & Antonio Carcaterra & Luca Cedola & Franco Rispoli, 2020. "Variational Control Approach to Energy Extraction from a Fluid Flow," Energies, MDPI, vol. 13(18), pages 1-20, September.
    3. Bertašienė, Agnė & Azzopardi, Brian, 2015. "Synergies of Wind Turbine control techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 45(C), pages 336-342.
    4. Wang, Jianing & Zhu, Hongqiu & Zhang, Yingjie & Cheng, Fei & Zhou, Can, 2023. "A novel prediction model for wind power based on improved long short-term memory neural network," Energy, Elsevier, vol. 265(C).
    5. Christoph M. Hackl & Pol Jané-Soneira & Martin Pfeifer & Korbinian Schechner & Sören Hohmann, 2018. "Full- and Reduced-Order State-Space Modeling of Wind Turbine Systems with Permanent Magnet Synchronous Generator," Energies, MDPI, vol. 11(7), pages 1-33, July.
    6. 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.
    7. Izquierdo, J. & Márquez, A. Crespo & Uribetxebarria, J. & Erguido, A., 2020. "On the importance of assessing the operational context impact on maintenance management for life cycle cost of wind energy projects," Renewable Energy, Elsevier, vol. 153(C), pages 1100-1110.
    8. Petković, Dalibor & Ćojbašič, Žarko & Nikolić, Vlastimir, 2013. "Adaptive neuro-fuzzy approach for wind turbine power coefficient estimation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 191-195.
    9. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Tan, Yong & Rao, Lei, 2022. "Anomaly detection and condition monitoring of wind turbine gearbox based on LSTM-FS and transfer learning," Renewable Energy, Elsevier, vol. 189(C), pages 90-103.
    10. Miguel A. Rodríguez-López & Luis M. López-González & Luis M. López-Ochoa & Jesús Las-Heras-Casas, 2018. "Methodology for Detecting Malfunctions and Evaluating the Maintenance Effectiveness in Wind Turbine Generator Bearings Using Generic versus Specific Models from SCADA Data," Energies, MDPI, vol. 11(4), pages 1-22, March.
    11. Kong, Xiaobing & Ma, Lele & Wang, Ce & Guo, Shifan & Abdelbaky, Mohamed Abdelkarim & Liu, Xiangjie & Lee, Kwang Y., 2022. "Large-scale wind farm control using distributed economic model predictive scheme," Renewable Energy, Elsevier, vol. 181(C), pages 581-591.
    12. 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.
    13. 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).
    14. Moradi, Hamed & Vossoughi, Gholamreza, 2015. "Robust control of the variable speed wind turbines in the presence of uncertainties: A comparison between H∞ and PID controllers," Energy, Elsevier, vol. 90(P2), pages 1508-1521.
    15. Zhu, Yongchao & Zhu, Caichao & Tan, Jianjun & Wang, Yili & Tao, Jianquan, 2022. "Operational state assessment of wind turbine gearbox based on long short-term memory networks and fuzzy synthesis," Renewable Energy, Elsevier, vol. 181(C), pages 1167-1176.
    16. Unai Elosegui & Igor Egana & Alain Ulazia & Gabriel Ibarra-Berastegi, 2018. "Pitch Angle Misalignment Correction Based on Benchmarking and Laser Scanner Measurement in Wind Farms," Energies, MDPI, vol. 11(12), pages 1-20, December.
    17. Qunli Wu & Huaxing Lin, 2019. "Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model," Sustainability, MDPI, vol. 11(3), pages 1-18, January.
    18. Xu, Zhi & Zhang, Ting & Li, Xiaojuan & Li, Yan, 2023. "Effects of ambient temperature and wind speed on icing characteristics and anti-icing energy demand of a blade airfoil for wind turbine," Renewable Energy, Elsevier, vol. 217(C).
    19. Yao Liu & Lin Guan & Fang Guo & Jianping Zheng & Jianfu Chen & Chao Liu & Josep M. Guerrero, 2019. "A Reactive Power-Voltage Control Strategy of an AC Microgrid Based on Adaptive Virtual Impedance," Energies, MDPI, vol. 12(16), pages 1-15, August.
    20. Wang, Han & Yan, Jie & Han, Shuang & Liu, Yongqian, 2020. "Switching strategy of the low wind speed wind turbine based on real-time wind process prediction for the integration of wind power and EVs," Renewable Energy, Elsevier, vol. 157(C), pages 256-272.

    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:gam:jeners:v:13:y:2020:i:1:p:184-:d:304011. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.