Augmented Kalman filter with a reduced mechanical model to estimate tower loads on an onshore wind turbine: a digital twin concept

Branlard, Emmanuel; Giardina, Dylan; Brown, Cameron S.

doi:10.5194/wes-2020-55

Cited by 2 publications

(2 citation statements)

References 16 publications

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“…Coupling sensor data with models of the turbine allows the creation of "digital twins" of wind turbines (Rinker et al, 2018). These software representations of hardware are now available as commercial services and are frequently used for tracking turbine fatigue loading and remaining lifetimes (Branlard et al, 2020). Research is ongoing on ways to use this data to make intelligent control decisions (Pettas et al, 2018).…”

Section: Opportunities For the Use Of Datamentioning

confidence: 99%

Grand Challenges in the Digitalisation of Wind Energy

Clifton

Barber

Bray³

et al. 2022

Preprint

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Abstract. The availability of large amounts of data is starting to impact how the wind energy community works. From turbine design to plant layout, construction, commissioning, and maintenance and operations, new processes and business models are springing up. This is the process of digitalisation, and it promises improved efficiency and greater insight, ultimately leading to increased energy capture and significant savings for wind plant operators, thus reducing the levelized cost of energy. Digitalisation is also impacting research, where it is both easing and speeding up collaboration, as well as making research results more accessible. This is the basis for innovations that can be taken up by end users. But digitalisation faces barriers. This paper uses a literature survey and the results from an expert elicitation to identify three common industry-wide barriers to the digitalisation of wind energy. Comparison with other networked industries and past and ongoing initiatives to foster digitalisation show that these barriers can only be overcome by wide-reaching strategic efforts, and so we see these as "Grand Challenges" in the digitalisation of wind energy. They are, first, the need to create reusable data frameworks; secondly, the need to connect people to data to foster innovation; and finally, the need to enable collaboration and competition between organisations. The Grand Challenges thus include a mix of technical and cultural aspects that will need collaboration between businesses, academia, and government to solve. Working to mitigate them is the beginning of a dynamic process that will position wind energy as an essential part of a global clean energy future.

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Section: Opportunities For the Use Of Datamentioning

confidence: 99%

Grand Challenges in the Digitalisation of Wind Energy

Clifton

Barber

Bray³

et al. 2022

Preprint

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“…In Fig. 6, we classified and referenced the latest case studies of DT usage reported in the literature for different innovative industries that come under the vision of Industry 4.0, i.e., manufacturing [60]- [63], automobile [64]- [67], aerospace [68]- [71], windfarm [72]- [75], and healthcare [76]- [79]. Moreover, Fig.…”

Section: B the Role Of Dt Across Industriesmentioning

confidence: 99%

Industrial Digital Twins at the Nexus of NextG Wireless Networks and Computational Intelligence: A Survey

Zeb¹,

Mahmood²,

Hassan³

et al. 2021

Preprint

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By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry 4.0 promotes integrating cyberphysical worlds through cyber-physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT is an emerging but conceptually different construct than CPS. Like CPS, DT relies on communication to create a highly-consistent, synchronized digital mirror image of the objects or physical processes. DT, in addition, uses builtin models on this precise image to simulate, analyze, predict, and optimize their real-time operation using feedback. DT is rapidly diffusing in the industries with recent advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research in next-generation (NextG) wireless technologies (e.g., 5G and beyond networks), various tools (e.g., age of information, federated learning, data analytics), and other promising trends in networked computing (e.g., edge and cloud computing). Moreover, we discuss the DT deployment strategies at different industrial communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT's adoption.

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Augmented Kalman filter with a reduced mechanical model to estimate tower loads on an onshore wind turbine: a digital twin concept

Cited by 2 publications

References 16 publications

Grand Challenges in the Digitalisation of Wind Energy

Grand Challenges in the Digitalisation of Wind Energy

Industrial Digital Twins at the Nexus of NextG Wireless Networks and Computational Intelligence: A Survey

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