Comparative analysis of offshore wind turbine blade maintenance: RL-based and classical strategies for sustainable approach

This study compares traditional methods like Corrective Maintenance (CM), Scheduled Maintenance (SM), and Condition-based Maintenance (CbM) with Reinforcement Learning (RL)-based offshore wind turbine (OWT) blade maintenance strategies. In order to address the dual challenge of minimizing carbon out...

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Veröffentlicht in:Reliability engineering & system safety 2025-01, Vol.253, p.110477, Article 110477
Hauptverfasser: Hendradewa, Andrie Pasca, Yin, Shen
Format: Artikel
Sprache:eng
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Zusammenfassung:This study compares traditional methods like Corrective Maintenance (CM), Scheduled Maintenance (SM), and Condition-based Maintenance (CbM) with Reinforcement Learning (RL)-based offshore wind turbine (OWT) blade maintenance strategies. In order to address the dual challenge of minimizing carbon output while managing maintenance costs and operational efficiency, the study presents a mathematical model intended to estimate carbon emissions associated with OWT maintenance activities. The ability of the RL-based strategy to reduce the risk of fatigue failure in OWT blades and account for wind speed variability in maintenance schedule optimization is assessed. In order to provide a sustainable maintenance solution this strategy balances the trade-offs between economic profit and environmental effect. The findings demonstrate how RL can provide a balanced approach to maintenance that enhances both operational performance and environmental sustainability.
ISSN:0951-8320
DOI:10.1016/j.ress.2024.110477