TY - JOUR
T1 - A system dynamics model of offshore wind farm degradation
T2 - Enabling operation and maintenance planning under foreseen asset management impacts
AU - Golestani, Nima
AU - Arzaghi, Ehsan
AU - Abbassi, Rouzbeh
AU - Garaniya, Vikram
AU - Meng, Huixing
N1 - Publisher Copyright:
© 2023 The Authors
PY - 2023/9
Y1 - 2023/9
N2 - This paper presents a system dynamics model to for the degradation of offshore wind farms (OWFs) optimise O&M planning and asset management, taking into account the effects of weather-related delays. The model predicts the future states of wind turbines, repairs, downtime expenses, failures, and production losses. Simulation results show the optimal scenario for effective intervention and the adaptability of the proposed model in controlling wind farm assets. By considering various maintenance planning approaches, the model identifies the factors affecting maintenance and minimises the impact of human response on O&M activities in OWFs.
AB - This paper presents a system dynamics model to for the degradation of offshore wind farms (OWFs) optimise O&M planning and asset management, taking into account the effects of weather-related delays. The model predicts the future states of wind turbines, repairs, downtime expenses, failures, and production losses. Simulation results show the optimal scenario for effective intervention and the adaptability of the proposed model in controlling wind farm assets. By considering various maintenance planning approaches, the model identifies the factors affecting maintenance and minimises the impact of human response on O&M activities in OWFs.
KW - Offshore wind farm
KW - Operation and maintenance
KW - Organisational-risk perception
KW - System degradation
KW - System dynamics
KW - Weather-related delays
UR - http://www.scopus.com/inward/record.url?scp=85166296448&partnerID=8YFLogxK
U2 - 10.1016/j.apor.2023.103685
DO - 10.1016/j.apor.2023.103685
M3 - Article
AN - SCOPUS:85166296448
SN - 0141-1187
VL - 138
JO - Applied Ocean Research
JF - Applied Ocean Research
M1 - 103685
ER -