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Results for Wind turbines

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  1. Data-driven condition monitoring approaches to improving power output of wind turbines

    Qian, P., Ma, X., Zhang, D. & Wang, J., 1/08/2019, In : IEEE Transactions on Industrial Electronics. 66, 8, p. 6012 - 6020 9 p.

    Research output: Contribution to journalJournal article

  2. A multi-scale model for studying failure mechanisms of composite wind turbine blades

    Ye, J., Chu, C., Cai, H., Hou, X., Shi, B., Tian, S., Chen, X. & Ye, J., 15/03/2019, In : Composite Structures. 212, p. 220-229 10 p.

    Research output: Contribution to journalJournal article

  3. Late Acceptance Selection Hyper-heuristic for Wind Farm Layout Optimisation Problem

    Abdulaziz, H., Elnahas, A., Daffalla, A., Noureldien, Y., Kheiri, A. & Özcan, E., 1/11/2018, 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE). IEEE, p. 1-5 5 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

  4. Wind turbine fault detection and identification through PCA-based optimal variable selection

    Wang, Y., Ma, X. & Qian, P., 10/2018, In : IEEE Transactions on Sustainable Energy. 9, 4, p. 1627-1635 9 p.

    Research output: Contribution to journalJournal article

  5. A condition monitoring system for an early warning of developing faults in wind turbine electrical systems

    Ma, X., Cross, P. & Qian, P., 1/12/2016, In : Insight – Non-Destructive Testing and Condition Monitoring. 58, 12, p. 663-670 8 p.

    Research output: Contribution to journalJournal article

  6. Simultaneous fault detection and sensor selection for condition monitoring of wind turbines

    Zhang, W. & Ma, X., 12/04/2016, In : Energies. 9, 4, 15 p., 280.

    Research output: Contribution to journalJournal article

  7. An overview on fault diagnosis and nature-inspired optimal control of industrial process applications

    Precup, R-E., Angelov, P., Jales Costa, B. S. & Sayed-Mouchaweh, M., 12/2015, In : Computers in Industry. 74, p. 75-94 20 p.

    Research output: Contribution to journalJournal article

  8. Condition monitoring of wind turbines based on extreme learning machine

    Qian, P., Ma, X. & Wang, Y., 12/09/2015, ICAC2015: Proceedings of the 21st International Conference on Automation and Computing. IEEE, p. 37-42 6 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

  9. Model-based and fuzzy logic approaches to condition monitoring of operational wind turbines

    Cross, P. & Ma, X., 02/2015, In : International Journal of Automation and Computing. 12, 1, p. 25-34 10 p.

    Research output: Contribution to journalJournal article

  10. Compressible Reynolds-Averaged Navier-Stokes analysis of wind turbine turbulent flows using a Fully-Coupled Low-Speed preconditioned multigrid solver

    Campobasso, S., Yan, M., Drofelnik, J., Piskopakis, A. & Caboni, M., 2014, ASME Turbo Expo 2014: turbine technical conference and exposition: oil and gas applications; organic rankine cycle power systems; supercritical CO2 power cycles; wind energy. The American Society of Mechanical Engineers, Vol. 3B. 15 p. GT2014-25562

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

  11. A novel condition monitoring and real-time simulation system for wind turbines

    Ma, X. & Cross, P., 4/02/2013, Proceedings of EWEA 2013 - European Wind Energy Conference & Exhibition. Vienna: EWEA, 9 p.

    Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paper

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