Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets
Metadatos
Mostrar el registro completo del ítemEditorial
Elsevier
Materia
Petri net Reinforcement learning Q-learning Offshore wind turbines Condition-based maintenance
Fecha
2022-12-05Referencia bibliográfica
Ali Saleh... [et al.]. Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets, Reliability Engineering & System Safety, Volume 231, 2023, 109013, ISSN 0951-8320, [https://doi.org/10.1016/j.ress.2022.109013]
Patrocinador
European Commission 859957Resumen
With the emerging monitoring technologies, condition-based maintenance is nowadays a reality for the wind
energy industry. This is important to avoid unnecessary maintenance actions, which increase the operation
and maintenance costs, along with the costs associated with downtime. However, condition-based maintenance
requires a policy to transform system conditions into decision-making while considering monetary restrictions
and energy productivity objectives. To address this challenge, an intelligent Petri net algorithm has been
created and applied to model and optimize offshore wind turbines’ operation and maintenance. The proposed
method combines advanced Petri net modelling with Reinforcement Learning and is formulated in a general
manner so it can be applied to optimize any Petri net model. The resulting methodology is applied to a case
study considering the operation and maintenance of a wind turbine using operation and degradation data.
The results show that the proposed method is capable to reach optimal condition-based maintenance policy
considering maximum availability (equal to 99.4%) and minimal operational costs.