This paper presents the state-of-the-art technologies and development trends of wind turbine drivetrains – the system that converts kinetic energy of the wind to electrical energy – in different stages of their life cycle: design, manufacturing, installation, operation, lifetime extension, decommissioning and recycling. Offshore development and digitalization are also a focal point in this study. Drivetrain in this context includes the whole power conversion system: main bearing, shafts, gearbox, generator and power converter. The main aim of this article is to review the drivetrain technology development as well as to identify future challenges and research gaps. The main challenges in drivetrain research identified in this paper include drivetrain dynamic responses in large or floating turbines, aerodynamic and farm control effects, use of rare-earth material in generators, improving reliability through prognostics, and use of advances in digitalization. These challenges illustrate the multidisciplinary aspect of wind turbine drivetrains, which emphasizes the need for more interdisciplinary research and collaboration.

This paper presents a multi-degree of freedom torsional model of drivetrain system as the digital twin model for monitoring the remaining useful lifetime of the drivetrain components. An algorithm is proposed for the model identification, which receives the torsional response and estimated values of rotor and generator torques, and calculates the drivetrain dynamic properties, e.g. eigenvalues, and torsional model parameters. The applications of this model in prediction of gearbox remaining useful lifetime is discussed. The proposed method is computationally fast, and can be implemented by integrating with the current turbine control and monitoring system without a need for a new system and sensors installation. A test case, using 5 MW reference drivetrain, has been demonstrated.

In this article a virtual sensor for online load monitoring and subsequent remaining useful life (RUL) assessment of wind turbine gearbox bearings is presented. Utilizing a Digital Twin framework the virtual sensor combines data from readily available sensors of the condition monitoring (CMS) and supervisory control and data acquisition (SCADA) system with a physics-based gearbox model. Different state estimation methods including Kalman filter, Least-square estimator, and a quasi-static approach are employed for load estimation. For RUL assessment the accumulated fatigue damage is calculated with the Palmgren–Miner model. A case study using simulation measurements from a high-fidelity gearbox model is conducted to evaluate the proposed method. Estimated loads at the considered intermediate and high-speed shaft bearings show moderate to high correlation (R = 0.50 − 0.96) to measurements, as lower frequency internal dynamics are not fully captured. The estimated fatigue damage differs by 5–15% from measurements.

The design of a medium‐speed drivetrain for the Technical University of Denmark (DTU) 10‐MW reference offshore wind turbine is presented. A four‐point support drivetrain layout that is equipped with a gearbox with two planetary stages and one parallel stage is proposed. Then, the drivetrain components are designed based on design loads and criteria that are recommended in relevant international standards. Finally, an optimized drivetrain model is obtained via an iterative design process that minimizes the weight and volume. A high‐fidelity numerical model is established via the multibody system approach. Then, the developed drivetrain model is compared with the simplified model that was proposed by DTU, and the two models agree well. In addition, a drivetrain resonance evaluation is conducted based on the Campbell diagrams and the modal energy distribution. Detailed parameters for the drivetrain design and dynamic modelling are provided to support the reproduction of the drivetrain model. A decoupled approach, which consists of global aero‐hydro‐servo‐elastic analysis and local drivetrain analysis, is used to determine the drivetrain dynamic response. The 20‐year fatigue damages of gears and bearings are calculated based on the stress or load duration distributions, the Palmgren‐Miner linear accumulative damage hypothesis, and long‐term environmental condition distributions. Then, an inspection priority map is established based on the failure ranking of the drivetrain components, which supports drivetrain inspection and maintenance assessment and further model optimization. The detailed modelling of the baseline drivetrain model provides a basis for benchmark studies and support for future research on multimegawatt offshore wind turbines.