Digital twin modeling for predictive maintenance of gearboxes in floating offshore wind turbine drivetrains

Moghadam, Farid Khazaeli; Rebouças, Geraldo F. de S.; Nejad, Amir R.

doi:10.1007/s10010-021-00468-9

Cited by 50 publications

(24 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Digital twin of wind farms is beneficial for monitoring and operating individual wind turbines remotely (Pimenta et al 2020). They enable cost-effective maintenance and ensure greater reliability of the components used to convert wind energy into electricity (Moghadam et al 2021). Oñederra et al (2019) outlined development of a digital twin for a medium voltage cable prototype in a wind farm which can be used to simulate its behavior and increase its lifespan in order to accomplish preventative maintenance.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive digital twin for offshore wind farms

et al. 2023

View full text Add to dashboard Cite

As wind turbines continue to grow in size, they are increasingly being deployed offshore. This causes operation and maintenance of wind turbines becoming more challenging. Digitalization is a key enabling technology to manage wind farms in hostile environments and potentially increasing safety and reducing operational and maintenance costs. Digital infrastructure based on Industry 4.0 concept, such as digital twin, enables data collection, visualization, and analysis of wind power analytic at either individual turbine or wind farm level. In this paper, the concept of predictive digital twin for wind farm applications is introduced and demonstrated. To this end, a digital twin platform based on Unity3D for visualization and OPC Unified Architecture (OPC-UA) for data communication is developed. The platform is completed with the Prophet prediction algorithm to detect potential failure of wind turbine components in the near future and presented in augmented reality to enhance user experience. The presentation is intuitive and easy to use. The limitations of the platform include a lack of support for specific features like electronic signature, enhanced failover, and historical data sources. Simulation results based on the Hywind Tampen floating wind farm configuration show our proposed platform has promising potentials for offshore wind farm applications.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Predictive digital twin for offshore wind farms

et al. 2023

View full text Add to dashboard Cite

show abstract

“…S-N curve is a plot that describes the relationship between stress (S) and the number of cycles to failures (N). S-N curve can be directly used to calculate the RUL [25,26] or with Miner´s rule to forecast the lifetime of bending gear tooth root, high speed shaft bearings (HSSB), and blades [27][28][29][30]. The number of stress cycles can be counted by the rainflow counting algorithm.…”

Section: Physics-based Modelmentioning

confidence: 99%

A review of failure prognostics for predictive maintenance of offshore wind turbines

Zhang

Vatn

Rasheed³

2022

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Offshore wind turbines (OWTs) are important facilities for wind power generation because of their low land use and high electricity output. However, the harsh environment and remote location of offshore sites make it difficult to conduct maintenance on turbines. To upkeep OWTs cost-effectively, predictive maintenance (PdM) is an appealing strategy for offshore wind industry. The heart of PdM is failure prognostics, which aims to predict an asset’s remaining useful life (RUL) based on condition monitoring (CM). To provide references to PdM of OWTs, this paper presents a systematic review of failure prognostic models for wind turbines. In this review, data-driven models, model-based models, and hybrid models are classified and presented for model selection. The findings reveal that it is promising to develop hybrid models in the future and combine the advantages of data-driven and model-based models. Currently, the internal combinations of machine learning methods and statistical approaches in data-driven models are more common than exterior linkages between data-driven models and model-based models. The limitations and strengths of different models are discussed, and opportunities for developing hybrid models are highlighted in the conclusion.

show abstract

“…The literature on digital twins in the energy sector has mainly focused on electricity grids in general, e.g., (Andryushkevich et al 2019;Steindl et al 2020;Zhang et al 2021). So far, little literature on digital twins has focused on renewable energy resources, although there are some discussions regarding digital twins for solar farms (Zohdi 2021) and wind farms (Ospina-Bohórquez et al 2022), the focuses are not on the development of digital twins, but on monitoring (Moghadam and Nejad 2022) or maintenance (Moghadam et al 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The applications of digital twin technology in wind energy in the literature mainly focus on the wind turbine, e.g., the blade (Fahim et al 2022), or wind farms (Ospina-Bohórquez et al 2022), with the main purposes of monitoring (Moghadam and Nejad 2022) or maintenance (Moghadam et al 2021). However, there are challenges in the development and implementation of most applications.…”

Section: Introductionmentioning

confidence: 99%

Potentials of game engines for wind power digital twin development: an investigation of the Unreal Engine

Sørensen

Jôrgensen

2022

Energy Inform

View full text Add to dashboard Cite

Digital twin technologies have become popular in wind energy for monitoring and what-if scenario investigation. However, developing a digital representation of the wind is challenging, especially due to the digital twin platform constraints. Game engines might be possible to solve this issue, especially since game engines have been used for product design, testing, prototyping, and also digital twins. Therefore, this study investigates the potential of developing a digital twin of wind power in the Unreal game engine. A case study of two types of wind turbines (Vestas V164-8 and Enercon E-126 7.580) and one location (Esbjerg, Denmark) is chosen for this study. The digital twin includes the environment with historical wind data and the visual representation of the wind turbine with a wind power production model and the estimated production in the given wind conditions of the area. The results show that game engines are viable for building entire digital twins where a realistic graphical user interface is required. Unreal Engine 5 provides the tools for modelling the landscape, surrounding water, and lighting. In addition, the Unreal Engine ecosystem provides vast amounts of content, such as 3D assets and game logic plugins, easing the digital twin development. The results prove that digital twins built in Unreal Engine 5 have great potential development of digital twins and user interfaces for communicating with a digital twin. The developed digital twin allows for further extension to benefit future digital twins utilizing wind turbines.

show abstract

Digital twin modeling for predictive maintenance of gearboxes in floating offshore wind turbine drivetrains

Cited by 50 publications

References 20 publications

Predictive digital twin for offshore wind farms

Predictive digital twin for offshore wind farms

A review of failure prognostics for predictive maintenance of offshore wind turbines

Potentials of game engines for wind power digital twin development: an investigation of the Unreal Engine

Contact Info

Product

Resources

About