Abstract
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.
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Acknowledgement
The authors would like to thank Naijapals for their financial support towards this study and the supervisory team for their support.
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Odofin, S., Sowale, A. (2017). Genetic Algorithm Systems for Wind Turbine Management. In: Benlamri, R., Sparer, M. (eds) Leadership, Innovation and Entrepreneurship as Driving Forces of the Global Economy. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-43434-6_12
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DOI: https://doi.org/10.1007/978-3-319-43434-6_12
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