# Improving the Strategy of Maintaining Offshore Wind Turbines through Petri Net Modelling
## 1 Introduction  
Wind power is one of the fastest-growing emerging industries in recent years. For example, 205 GW of wind power capacity has been built in Europe to date, accounting for 15% of the EU's total electricity demand in 2019 [1]. Nowadays, although most wind turbines are on land, an increasing number of wind turbines are being deployed offshore due to better wind resources, less visual impact, and less noise pollution and land use issues at offshore sites [2]. However, the current high operation and maintenance (O&M) costs of offshore wind turbines (OMTs) are challenging the sustainable development of the offshore wind industry [3]. Therefore, how to reduce the O&M costs of OMTs, thereby lowering the cost of offshore wind power, has become an urgent issue that needs to be addressed. The maintenance of OMTs is affected by many factors, such as component reliability, site accessibility, weather conditions, maintenance strategies, availability of spare parts, vessels, tools, maintenance crews, etc. Hence, it is essential to carefully schedule a maintenance plan beforehand. To help the operators schedule an optimal maintenance plan, much effort has been made by scholars in recent years. For example, [PERSON] and [PERSON] investigated the influences of different maintenance strategies and demonstrated the positive contribution of the condition monitoring systems (CMSs) to cost reduction [4]. [PERSON] and [PERSON] compared scheduled maintenance and condition-based maintenance using Markov chains [5]. [PERSON] et al. investigated the impact of reliability on the O&M costs of OMTs [6]. The factors that have a direct impact on the O&M costs of offshore wind farms have also been investigated. [PERSON] et al. investigated the impact of the accuracy of the weather forecast, maintenance strategies, and maintenance fleet on the availability of offshore wind farms [7]. [PERSON] et al. developed a model to optimise the maintenance of offshore wind farms with the consideration of the number of maintenance personnel, the types of maintenance vessels, the use of a helicopter for site access, and the location of accommodation [8]. [PERSON] considered whether the maintenance vessels should be purchased or chartered based on the site locations and also discussed the influences of wave height and vessel fleet size on the maintenance process [9]. [PERSON] et al. studied the impact of multiple environmental factors on the maintenance costs of offshore wind farms [10]. In their research, four different transportation systems with different speeds, access restrictions, costs, and carrying capabilities were considered. Much research has been conducted for reducing the O&M costs of offshore wind farms. For example, [PERSON] et al. developed a mathematical tool to help the operators better estimate the O&M costs of offshore wind farms [11]. [PERSON] et al. used a multi-objective particle swarm optimisation method to optimise the opportunistic maintenance strategy of a wind farm [12]. [PERSON] and [PERSON] also proposed an approach to optimise the maintenance schedules for an OWT [13], which considered individual wind turbine components and grouped those based on their failure patterns. A more detailed review of the existing research on modelling the maintenance activities of OMTs can be found in [14]. The outcomes of these studies have provided strong support for decision-making during the operation of offshore wind farms. However, although corrective maintenance and preventive maintenance strategies are used in combination in the maintenance practice of offshore wind farms, discrepancies still exist between the mathematical models developed and actual maintenance activities and many basic questions related to the O&M of OMTs have not been fully answered. As mentioned in [14], one of the main reasons for this is the lack of a more advanced mathematical modelling method to accurately describe the relevant maintenance activities.