Predictive digital twin for offshore wind farms

Haghshenas, Amirashkan; Hasan, Agus; Osen, Ottar L.; Mikalsen, Egil Tennfjord

doi:10.1186/s42162-023-00257-4

Cited by 45 publications

(29 citation statements)

References 36 publications

(31 reference statements)

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“…During the implementation phase, the digital twin system is seamlessly integrated with real‐world container truck data and its surrounding environment. To illustrate its practical applications, the digital twin system has been utilized for the detection, monitoring, and prediction of both faults (Bhagavathi et al, 2023; Hasan, Widyotriatmo, et al, 2023) and cyberattacks (Ambarita et al, 2023; Kuncara et al, 2023).…”

Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Leveraging digital twin for autonomous docking of a container truck with stabilization control

Widyotriatmo,

Kuncara,

Amri

et al. 2024

Journal of Field Robotics

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This paper describes the design, development, and implementation of a high‐precision autonomous docking control system for a container truck based on a digital twin approach. The digital twin is used to simulate the dynamic behavior of the physical truck and to design the controller by providing a virtual platform to test, validate, and optimize control strategies and algorithms before their deployment in the actual system. To this end, a cascade of a nonlinear observer and an unscented Kalman filter is used to estimate the state variables of the physical truck for point‐stabilization and orientation controls during the autonomous docking process. The docking motion involves two stabilization problems: point stabilization for smooth motion from the initial configuration to the docking slot, and orientation control to deliver the container truck to the final docking position with a margin of error of 5 cm for position and 0.0087 rad for orientation. The stability of both controllers is investigated, and simulations and experiments are conducted to demonstrate the accuracy of the proposed method in a container terminal environment.

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Section: Simulation and Experimental Resultsmentioning

confidence: 99%

Leveraging digital twin for autonomous docking of a container truck with stabilization control

Widyotriatmo,

Kuncara,

Amri

et al. 2024

Journal of Field Robotics

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“…Accurate numerical tools (models or codes) are, of course, essential for wind turbine design, to simulate inflow and operating conditions, compute loads, evaluate structural/material designs, etc. However, these tools can also be quite useful for wind farm operations -namely for asset management to support decision-making throughout each turbine's lifetime [16], [48], [49]. These numerical design tools can be used to develop a model that represents the physical asset (the operating wind turbine), and such a model is referred to as a "digital twin".…”

Section: Operation: Digital Twin Models Of Blade and Turbinesmentioning

confidence: 99%

Composite materials in wind energy: design, manufacturing, operation, and end-of-life

Griffith,

Cao,

et al. 2023

IOP Conf. Ser.: Mater. Sci. Eng.

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Wind energy has consistently grown over recent decades and has grown in many aspects including in terms of installed capacities, turbine size, blade length, and grid penetration. Along with this, wind energy is one of the largest producers of composite structures, and as a result is one of the largest users of composite materials. For wind energy applications, composite materials require high reliability, low-cost, and near-term and future industry goals are to reuse or recycle composite materials. These requirements are quite challenging as wind energy faces a challenging operating environment, which puts great pressure on wind blade materials over their entire life cycle. This paper aims to examine the challenges of composite materials for wind energy applications, and to highlight a few research studies that offer potential new solutions and new insights across the entire life cycle of composites for wind energy systems ranging from the design phase, to manufacturing, to operation, and finally to the end-of-life phase.

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“…Göllner et al (2021) generated the dynamic simulation model automatically based on the standardized and interoperable information model where all necessary data is mapped into the AAS structure. The role of AASs in life cycle management (Marcon et al, 2019;Deuter and Imort, 2020;Rauh et al, 2022) This section provides findings from a comprehensive investigation of a previous case study conducted by one of the authors in Haghshenas et al (2023). The case study is based on the Hywind Tampen floating wind farm project, developed by Equinor, which aimed to implement a digital twin in offshore wind farms.…”

Section: The Interoperable Digital Twin Framework For the Offshore Wi...mentioning

confidence: 99%

“…Data sources that can be utilized in the construction of a digital twin may include visual data, measurement data, historical data, and etc. In this case study, these data sources are classified into four categories: static data, simulated data, live data, and historical data (Haghshenas et al, 2023).…”

Section: Data Sourcementioning

confidence: 99%

Industry 4.0 Digital Twins in Offshore Wind Farms

Ambarita,

Karlsen,

Scibilia

et al. 2023

Preprint

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Abstract. The use of digital twins in offshore wind farms presents a major opportunity to advance autonomous operations and optimize productivity. By creating virtual replicas of physical assets and systems, digital twins allow for real-time monitoring, predictive maintenance, and efficient decision-making. In the context of Industry 4.0, wind turbines are required not only to be remotely monitored and controlled through real-time data during operation, but also to manage the whole life cycle within the entire value chain. To accomplish this, the implementation of digital twin frameworks in accordance with Industry 4.0 standards is crucial. Motivated by the advanced technologies related to industrial digital twins in the context of Industry 4.0 implemented in the manufacturing sector, this paper presents findings from a study investigating the potential for transferring knowledge of industrial digital twins to offshore wind farm context. To gain a deeper understanding of the digital twin concept in the context of offshore wind applications, we conducted extensive literature studies. Specifically, we examined frameworks used in both the manufacturing industry and offshore wind industry. Our goal is to identify commonalities and differences between these frameworks, and to determine how they could be adapted to the unique requirements of the offshore wind sector. The Asset Administration Shell (AAS), which is a key concept of the Reference Architecture Model for Industry 4.0 (RAMI 4.0), provides a standardized and modular approach to representing and managing assets in industrial systems. By adopting AAS, offshore wind farms could improve the scalability, adaptability, and interoperability of their digital twin systems, and also enable more efficient and effective operation and maintenance of the turbines. Based on our findings, we concluded that implementing the AAS could be a promising option for enhancing the functionality of digital twins in offshore wind farms, and for achieving interoperability in line with the standards of Industry 4.0.

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Predictive digital twin for offshore wind farms

Cited by 45 publications

References 36 publications

Leveraging digital twin for autonomous docking of a container truck with stabilization control

Leveraging digital twin for autonomous docking of a container truck with stabilization control

Composite materials in wind energy: design, manufacturing, operation, and end-of-life

Industry 4.0 Digital Twins in Offshore Wind Farms

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