E3S Web Conf.
Volume 233, 20212020 2nd International Academic Exchange Conference on Science and Technology Innovation (IAECST 2020)
|Number of page(s)||7|
|Section||NESEE2020-New Energy Science and Environmental Engineering|
|Published online||27 January 2021|
A Systematic Framework for Maintenance Scheduling and Routing for Off-Shore Wind Farms by Minimizing Predictive Production Loss
1 NSF I/UCR Center for Intelligent Maintenance Systems, Department of Mechanical Engineering, University of Cincinnati, PO Box 210072, Cincinnati, Ohio 45221-0072, USA
2 Foxconn Industrial Internet, No. 2, Donghuan Er Road, Longhua District, Shenzhen, 518109, China
a Corresponding author: email@example.com
Maintenance scheduling and vessel routing are critical for the off-shore wind farm to reduce maintenance costs. In this research, a systematic framework that takes the advantage of predictive analysis for off-shore wind farm maintenance optimization is sketched and the optimization results are presented. The proposed framework consists of three different functional modules - the prognostic and diagnostic (P&D) module, the wind power prediction module, and the maintenance optimization module. The P&D module predicts and diagnoses the system failures based on the operational data of the wind turbine and generates the maintenance tasks for execution. The power prediction module predicts the weather conditions and the production of the wind turbine in the next 1-3 days, which will be helpful for maintenance task prioritization and scheduling. The optimization module absorbs information from the previous two modules as input and optimizes the overall maintenance costs. Comparing with the previous research works, this framework optimizes the maintenance cost in a more challenging situation by considering the predicted remaining useful life from the P&D module and also the future weather condition from the wind power prediction module. In the proposed framework, the maintenance scheduling and the vessel routing are optimized collaboratively with the consideration of real-time production loss. The result of the proposed framework is demonstrated on an off-shore wind farm and reduced maintenance cost is reported.
© The Authors, published by EDP Sciences 2021
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