# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 4 Dynamic PN Modelling of the O&M of an Offshore Wind Turbine
### Recovery and Maintenance Petri Net (RMPN)  
The RMPN is developed for simulating the process of preparing and conducting the maintenance when any subsystem fails, or a subsystem fault is detected by the CMS or revealed during an \"advanced service\". As shown in Figure 7, once a subsystem failure occurs or a subsystem fault is detected by the CMS, a repair request will be made immediately via Transition \"M1\". Then, the maintenance team will be informed, and a meeting will be arranged for planning the maintenance. In the study, it is assumed that 12 h, indicated by Transition \"M2\", are needed to perform these activities. It is also assumed in this study that another 12 h, denoted by Transition \"M3\", are required for planning and approving the maintenance. The preparation for the maintenance is started by producing a token in both Places \"Charter vessel\" and \"Organise crews, tools and \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Subsystem**} & \multicolumn{3}{c}{**Probabilities**} \\ \cline{2-4}  & **Before Using CMS** & **After Using a Basic CMS** & **After Using a Premier CMS** \\ \hline \hline Rotor & 0 & 0.6 & 0.997 \\ \hline Drivetrain & 0 & 0.7 & 0.997 \\ \hline Power system & 0 & 0.8 & 0.997 \\ \hline Bearing system & 0 & 0.7 & 0.997 \\ \hline Braking system & 0 & 0.8 & 0.997 \\ \hline Structures & 0 & 0.4 & 0.997 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Probabilities that the faults can be detected by the condition monitoring system (CMS). Figure 6: Detection Petri Net (DPN). -spare parts\". It is assumed that 10 to 30 days, denoted by Transition \"M4\", are required for checking weather conditions, identifying maintenance window, and chartering the appropriate maintenance vessel, and 7 to 14 days, denoted by Transition \"M5\", are required for organising maintenance crews, collecting maintenance tools and preparing spare parts. Both of the transition times of \"M4\" and \"M5\" are assumed to follow a normal distribution. As soon as both maintenance vessel and maintenance crews, tools, and spare parts are available, a token will be produced in Place \"Travel to site\" via Transition \"M6\". The sea transportation time is assumed to be 3 h and indicated by Transition \"M7\" in this study. In reality, it depends on the offshore distance of the wind farm and the cruise speed of the vessel and can be easily varied in the model. Upon arrival at the site, some preparations need to be undertaken (e.g., navigate the position of the vessel and lift the platform of a Jack-up vessel out of the water surface). This will take about 2 to 4 h depending on the situation. This preparation time is denoted by Transition \"M8\". Then, the maintenance will be conducted. The maintenance time, indicated by Transition \"M9\", is assumed one day for repairing all faults or seven days for recovering the subsystem from the failure. This transition time can be adapted for the offshore wind farm being considered at the time using available practical data. It is worth noting that the wind turbine should remain stopped and not allowed to be started during the entire maintenance period. Finally, a token will be produced in Place \"Maintenance completed\" and the RMPN will be removed from the model when the maintenance is completed. The new health state of the corresponding subsystem will be fed back to the SPN. The token in the corresponding place, \"Subsystem failure\" or \"Subsystem fault\", will be removed and a token will be passed to corresponding Place \"Normal operation\" in the SPN. It is worth noting that all parameters used in the models developed above are only for facilitating model development. Although they are extracted from open literature, they should be updated in the practical application based on the concrete situation of the OWT of interest. In real life, the values of these model parameters are dependent on many factors. This is why a significant discrepancy was observed in the survey [6].