An integrated operational system to reduce O&M cost of offshore wind farms
Kai Wang (Wuhan University of Technology, TU Delft - Transport Engineering and Logistics)
X. Jiang (TU Delft - Transport Engineering and Logistics)
R.R. Negenborn (TU Delft - Transport Engineering and Logistics)
Xuexin Yan (Wuhan University of Technology)
Y. Yuan (Wuhan University of Technology)
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Abstract
Offshore wind is a relatively new industry and it is generally more expensive to generate electricity than many alternative renewable sources. Operation & Maintenance (O&M) makes up a significant part of the overall cost of running Offshore Wind Turbines (OWT). Since the O&M associated responsibility is shared among turbine manufacturers, wind farm operators and the offshore transmission owners, this has inevitably led to lack of information, duplication of effort and less efficiency. Big data analytics is one great technique that will drive future growth. In this paper, an integrated operational system of offshore wind farm is proposed deploying big data analytics. Firstly, the current state of the O&M of offshore wind farm and the big data analytics are introduced. Afterwards, a predictive maintenance model and a maintenance implementation model are proposed, and an integrated operational system is developed incorporating those two models in order to optimize maintenance planning and implementation. Finally, the possible contribution of such a system to a more effective O&M of offshore wind farm is discussed.