Farid Khazaeli Moghadam ^1* ARVIND KEPRATE ² Zhen Gao ³

• ¹ Nexans (Norway), Oslo, Norway
• ² Oslo Metropolitan University, Oslo, Norway
• ³ Shanghai Jiao Tong University, Shanghai, Shanghai Municipality, China

This research topic also delves into powertrain degradation and control. In Yaw Misalignment in Powertrain Degradation Modelling for Wind Farm Control in Curtailed Conditions, Moghadam et al. examine how yaw misalignment affects powertrain components and propose a damage-aware control framework for efficient power distribution within wind farms. This study highlights the integration of degradation metrics into farm-level control, demonstrating how digital twin models can simultaneously address efficiency and durability through advanced control strategies, and allow operators to make informed decisions about the power setpoints of individual turbines within large farms. Together, these studies represent a comprehensive approach to tackling the challenges of digital twin modeling for predictive maintenance in wind energy. They collectively address the three core layers of digital twin realization-data acquisition, dynamic model processing, and decision support-each layer contributing to a robust framework for optimizing turbine performance. As digital twin technology continues to mature, these contributions highlight the technical challenges and the immense potential of predictive maintenance, steering the wind energy industry towards a more sustainable and efficient future.