Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets. (March 2023)

• Record Type: Journal Article
• Authors: Saleh, Ali; Chiachío, Manuel; Salas, Juan Fernández; Kolios, Athanasios
• Is Part Of: Reliability engineering & system safety. Volume 231(2023)
• Publication Date: 2023-03
• Subjects: Petri net -- Reinforcement learning -- Q-learning -- Offshore wind turbines -- Condition-based maintenance; Reliability (Engineering) -- Periodicals; System safety -- Periodicals; Industrial safety -- Periodicals; Fiabilité -- Périodiques; Sécurité des systèmes -- Périodiques; Sécurité du travail -- Périodiques; 620.00452
• DOI: 10.1016/j.ress.2022.109013
• Languages: English
• ISSNs: 0951-8320

Abstract

Abstract: 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. Highlights: An intelligent Petri net ( i PN) method by integrating Petri nets with Reinforcement Learning. i PNs can maximize the reliability and availability by finding an optimal O&M policy. Reinforcement learning is adapted to incorporate multiple and simultaneous actions. The method is demonstrated on an optimal O&M model for an offshore wind turbine. The resulting method works as an optimal self-adaptive expert system.