Genetic Algorithm Systems for Wind Turbine Management

Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)


In this paper, the importance of wind turbine renewable energy management is important. Wind turbine is sophisticated, expensive and complicated in nature. Fault diagnosis is vital for wind turbine healthy operational state for reliability that is of high priority prognostic for effective management system. A novel algorithm is proposed to optimise the observer monitoring system performance to support practical operation. Reducing unplanned maintenance costs for uninterrupted healthy reliable operations will aid the online monitoring of the turbine behaviour.


Wind turbine Genetic algorithm Optimisation Observation 



The authors would like to thank Naijapals for their financial support towards this study and the supervisory team for their support.


  1. Bertling, L., & Ribrant, J. (2006). Survey of failure systems with focus on Swedish wind power plants during 1997–2005. IEEE Transactions on Energy Conversion, 22(1), 167–173.Google Scholar
  2. Engelbrecht, A. P. (2007). Computational intelligence. Chichester, West Sussex: Wiley.CrossRefGoogle Scholar
  3. Kai, S., Gao, Z., & Odofin, S. O. (2015). Robust sensor fault estimation for induction motors via augmented observer and GA optimisation technique. In International Conference on Mechatronics and Automation, August 2–5 (pp. 1727–1732).Google Scholar
  4. Kusiak, A., & Li, W. (2011). The prediction and diagnosis of wind turbine faults. Renewable Energy, 36, 16–23. Elsevier Ltd.CrossRefGoogle Scholar
  5. Nelson, V. (2009). Wind energy: Renewable energy and the environment. CRC Press/ Global Wind Statistics (GWEC) 2014. Global Wind Energy Council. Retrieved February 25, 2015, from Scholar
  6. Odofin, S., Gao, Z., & Sun, K. (2015). Robust fault diagnosis for wind turbine systems subjected to multi-faults. International Journal of Electrical, Computer, Energetic, Electronic and Communication Engineering WASET, 9(2).Google Scholar
  7. Odofin, S. O., Ghassemlooy, Z., Kai, S., & Gao, Z. (2014). Simulation study of fault detection and diagnosis for wind turbine system. PGNet. In 15th Annual Postgraduate Symposium on the Convergence of Telecommunications, Network and Broadcasting, Liverpool, UK, June 2014.Google Scholar
  8. Tchakoua, P., Wamkeue, R., Ouhrouche, M., Slaoui-Hasnaoui, F., Tameghe, T. A., & Ekemb, G. (2014). Wind turbine condition monitoring: State-of-the-art review, new trends, and future challenges. Energies, 7(4), 2595–2630.CrossRefGoogle Scholar
  9. Zhu, Y., & Gao, Z. (2014). Robust observer-based fault detection via evolutionary Optimization with applications to wind turbine systems. In Proc. IEEE 9th Conference on Industrial Electronics and Applications, Hangzhou (pp. 1627–1632).Google Scholar

Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  1. 1.Faculty of Engineering and EnvironmentNorthumbria University NewcastleNewcastle-Upon-TyneUK
  2. 2.Faculty of Engineering and EnvironmentNorthumbria University NewcastleNewcastle-Upon-TyneUK

Personalised recommendations