A Stochastic Petri Net Model for O&M Planning of Floating Offshore Wind Turbines

• Main Authors: Tobi Elusakin, Mahmood Shafiee, Tosin Adedipe, Fateme Dinmohammadi
• Format: Article
• Language: English
• Published: MDPI AG 2021-02-01
• Series: Energies
• Subjects: floating offshore wind turbine (FOWT); operation and maintenance (O&M); Petri network (PN); spar buoy platform; mooring and anchoring; reliability
• Online Access: https://www.mdpi.com/1996-1073/14/4/1134

With increasing deployment of offshore wind farms further from shore and in deeper waters, the efficient and effective planning of operation and maintenance (O&M) activities has received considerable attention from wind energy developers and operators in recent years. The O&M planning of offshore wind farms is a complicated task, as it depends on many factors such as asset degradation rates, availability of resources required to perform maintenance tasks (e.g., transport vessels, service crew, spare parts, and special tools) as well as the uncertainties associated with weather and climate variability. A brief review of the literature shows that a lot of research has been conducted on optimizing the O&M schedules for fixed-bottom offshore wind turbines; however, the literature for O&M planning of floating wind farms is too limited. This paper presents a stochastic Petri network (SPN) model for O&M planning of floating offshore wind turbines (FOWTs) and their support structure components, including floating platform, moorings and anchoring system. The proposed model incorporates all interrelationships between different factors influencing O&M planning of FOWTs, including deterioration and renewal process of components within the system. Relevant data such as failure rate, mean-time-to-failure (MTTF), degradation rate, etc. are collected from the literature as well as wind energy industry databases, and then the model is tested on an NREL 5 MW reference wind turbine system mounted on an OC3-Hywind spar buoy floating platform. The results indicate that our proposed model can significantly contribute to the reduction of O&M costs in the floating offshore wind sector.