Results for Wind turbines

Publications & Outputs

1. Intelligent Condition Monitoring of Wind Power Systems: State of the Art Review

   Benbouzid, M., Berghout, T., Sarma, N., Djurović, S., Wu, Y. & Ma, X., 20/09/2021, In: Energies. 14, 18, 33 p., 5967.

   Research output: Contribution to Journal/Magazine › Literature review › peer-review

2. 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 Journal/Magazine › Journal article › peer-review

3. 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 Journal/Magazine › Journal article › peer-review

4. 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/ISSN › Conference contribution/Paper › peer-review

5. 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 Journal/Magazine › Journal article › peer-review

6. 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 Journal/Magazine › Journal article › peer-review

7. 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 Journal/Magazine › Journal article › peer-review

8. 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 Journal/Magazine › Journal article › peer-review

9. 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/ISSN › Conference contribution/Paper › peer-review

10. 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 Journal/Magazine › Journal article › peer-review

11. 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/ISSN › Conference contribution/Paper › peer-review

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