Baumgärtner, DavidDavidBaumgärtnerKaczenski, JonasJonasKaczenskiMackeprang, JürgenJürgenMackeprangPracejus, ThomasThomasPracejus2025-08-052025-08-052025https://publica.fraunhofer.de/handle/publica/49018510.1088/1742-6596/3025/1/0120162-s2.0-105009163783Offshore wind energy is emerging as a crucial part of the global energy transition, yet the complexity and cost of operations and maintenance (O&M) continue to challenge the sector. This study introduces a novel analytical framework that leverages vessel and helicopter movement data - captured through AIS and ADS-B signals - and integrates hourly ERA5 weather data to comprehensively evaluate O&M performance at offshore wind farms (OWFs). By automatically identifying key operational events such as turbine transfers, port departures, and idling periods, the methodology computes a suite of key performance indicators that capture both the effectiveness (i.e., workload capacity under varying weather conditions) and the efficiency (i.e., transit, idle, and transfer times) of diverse logistical concepts. Analysis across multiple OWFs, including a detailed case study for the Butendiek wind farm, reveals that while efficiency scores remain relatively consistent, effectiveness varies significantly - particularly in response to weather constraints and fleet composition. The transition from a solely vessel-based strategy to a mixed fleet incorporating service operation vessels, crew transfer vessels, and helicopters resulted in improved workload capacity and enhanced overall performance, despite minor trade-offs in operational efficiency. These findings demonstrate the potential of integrating real-time movement and weather data to optimize O&M strategies, thereby reducing downtime and operational costs of offshore wind farms.enfalsejournal article