Use of probabilistic modelling to investigate optimal maintenance strategies in the life extension period of offshore wind turbines

Project Description:

This Research Project is part of the Aura CDT’s Reliability and Health Monitoring Cluster.

The typical life span of a wind turbine is twenty years and many of the earliest installed offshore turbines are nearing the end of their design life. At this stage there are three options available to wind farm owners, decommissioning of the site, repowering and life extension. Decommissioning involves removing all parts of the wind farm and restoring the seabed to its prior condition and repowering replaces, partially or totally, the old turbines by newer and more efficient ones. Both of these strategies have large financial and environmental implications. Lifetime extension due to its cost effectiveness, with no physical alterations and longer asset life span, is expected to become an essential part of the future wind industry. However, when turbines reach the end of their design life many of their components and subsystems will be suffering from wear and degradation, dependent upon maintenance performed.

The aim of this project is to develop models of offshore turbines which will incorporate the details of the turbines structure and the complexities of its operation giving an accurate prediction of turbine availability and degradation over time. Due to the uncertainty of inputs into the model such as the rate of degradation of components etc these will be represented by probability distributions. It has been shown that Petri Nets (PNs) are an efficient method for modelling turbines (Yan et al 2023) and hence it is expected that these will be adopted for this project. Using this approach models of the subsystems can be developed and combined to model the whole turbine, facilitating determination of the turbines state at the end of its design life. In order to achieve this, it will be necessary to code the PN’s and simulate the turbine performance. The models will then be further developed to investigate the various maintenance procedures in the extended life period, Dunnett and Leigh (2014). Not only does inspection and repair of different turbine components often require different equipment, expertise, service vessels etc, but farms with multiple turbines, of varying number, age, specifications etc, add complexities. The models for the turbines developed will be integrated to consider the maintenance of wind farms of varying size and complexity and optimal maintenance strategies determined.

The results will enable optimal maintenance management for turbines and farms, giving strategic direction in the future planning and operation of Off-shore Wind Farms during the life extension period.