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Operation and maintenance management for offshore wind farms integrating inventory control and health information

Author

Listed:
  • Li, Mingxin
  • Jiang, Xiaoli
  • Carroll, James
  • Negenborn, Rudy R.

Abstract

Effective operation and maintenance (O&M) management is significant for enhancing the economic performance of offshore wind farms. Despite recent research progress in O&M, there remains a gap in integrating health prognostics and spare parts inventory into decision-making processes at the scale of offshore wind farms. To bridge this gap, this paper develops an optimisation framework integrating these aspects to establish cost-effective joint maintenance and inventory policies. In the framework, a maintenance policy is firstly developed to plan maintenance actions based on component health and maintenance opportunities. Meanwhile, in order to support maintenance implementation, a multi-echelon inventory network using (s, S) policies is proposed to store diverse units across distinct warehouses. A genetic algorithm (GA) is then employed to identify the optimal policy, aiming to minimise overall costs. Upon developing the optimisation framework, in order to illustrate the application of the proposed approach in practice, a numerical simulation of a generic offshore wind farm in the North Sea is performed. Results demonstrate that comprehensive O&M management considering interrelationship between maintenance and inventory policies reduces overall costs, showcasing its capacity in strengthening the economic performance. Finally, sensitivity analysis is performed to investigate the most influential O&M factors, providing actionable insights for O&M management.

Suggested Citation

  • Li, Mingxin & Jiang, Xiaoli & Carroll, James & Negenborn, Rudy R., 2024. "Operation and maintenance management for offshore wind farms integrating inventory control and health information," Renewable Energy, Elsevier, vol. 231(C).
  • Handle: RePEc:eee:renene:v:231:y:2024:i:c:s0960148124010383
    DOI: 10.1016/j.renene.2024.120970
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