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Identification and Optimization of Most Relevant Variables When Creating a Maintenance Strategy of an Offshore Wind Farm

[+] Author Affiliations
Ana Beatriz Gomes Zanforlin, Adriana Miralles Schleder, Marcelo Ramos Martins

University of São Paulo, São Paulo, Brazil

Paper No. OMAE2017-61776, pp. V03BT02A021; 8 pages
  • ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering
  • Volume 3B: Structures, Safety and Reliability
  • Trondheim, Norway, June 25–30, 2017
  • Conference Sponsors: Ocean, Offshore and Arctic Engineering Division
  • ISBN: 978-0-7918-5766-3
  • Copyright © 2017 by ASME


A lot has been researched recently in order to enable economically feasible use of offshore wind energy. Although these figures have been falling, offshore wind energy generation has in average still much higher costs associated with the inherent drawbacks of installing and operating assets at the sea’s hostile environment. As much of these costs are related to unplanned maintenance tasks, one promising approach to make wind energy more competitive is to optimize the resources involved in it. This paper was developed with the purpose of analyzing the viability of an algorithm that offers valuable information when defining a maintenance strategy for the operation of an offshore wind farm, aiming at the availability and the expected profit optimization, with a different approach than usual. Initially, an algorithm to conduct a reliability, availability and maintainability (RAM) analysis was created based on a Monte Carlo Simulation (MCS). Given a simplified wind farm model, as well as its components’ failure data and configuration, it is possible to obtain its availability and energy production costs. The algorithm was validated by comparing known failure data with the stochastically obtained after running the algorithm. A case study was defined based on extensive literature research and the simulation was executed considering restrictions typically found in modern wind farms. A sensitivity analysis was conducted in order to understand how each model’s parameter affects the energy production costs. Given this analysis, it was possible to determine the most relevant optimization variables when creating a maintenance strategy. Following, an algorithm for optimizing those parameters is presented.

Copyright © 2017 by ASME



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