Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

Kooning, Jeroen D. M. De; Stockman, Kurt; Maeyer, Jeroen De; Laguna, Antonio Jarquin; Vandevelde, Lieven

doi:10.3390/pr9122224

Cited by 24 publications

(9 citation statements)

References 158 publications

(241 reference statements)

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“…Botz et al (2019) conducted research to apply digital twin framework by gathering vital data from attentively chosen spots of hybrid wind turbine structure for updating material models of the wind turbine in order to improve maintenance and operating parameters and extend the turbine's useful life. In another publication, Kooning et al (2021) presented a summary of recent research on modelling methodologies to build a digital twin for a wind turbine by considering the components, aerodynamics, structural and mechanics, power electronic converters, pitch and yaw systems. Furthermore, Pimenta et al (2020) created a digital twin by using SCADA to create a feasible trustworthy numerical model of a floating wind turbine.…”

Section: Literature Reviewmentioning

confidence: 99%

Predictive digital twin for offshore wind farms

et al. 2023

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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.

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Section: Literature Reviewmentioning

confidence: 99%

Predictive digital twin for offshore wind farms

et al. 2023

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“…De Kooning et al (2021) devised a DTT-based advanced structure and a virtual replica with the minimum computing load. A DTT-based wind energy conversion system was established, reaching an appropriate balance between model fidelity and computing load [ 85 ].…”

Section: Methodsmentioning

confidence: 99%

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Yang

et al. 2022

Sensors

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Digital twins technology (DTT) is an application framework with breakthrough rules. With the deep integration of the virtual information world and physical space, it becomes the basis for realizing intelligent machining production lines, which is of great significance to intelligent processing in industrial manufacturing. This review aims to study the application of DTT and the Metaverse in fluid machinery in the past 5 years by summarizing the application status of pumps and fans in fluid machinery from the perspective of DTT and the Metaverse through the collection, classification, and summary of relevant literature in the past 5 years. The research found that in addition to relatively mature applications in intelligent manufacturing, DTT and Metaverse technologies play a critical role in the development of new pump products and technologies and are widely used in numerical simulation and fault detection in fluid machinery for various pumps and other fields. Among fan-type fluid machinery, twin fans can comprehensively use technologies, such as perception, calculation, modeling, and deep learning, to provide efficient smart solutions for fan operation detection, power generation visualization, production monitoring, and operation monitoring. Still, there are some limitations. For example, real-time and accuracy cannot fully meet the requirements in the mechanical environment with high-precision requirements. However, there are also some solutions that have achieved good results. For instance, it is possible to achieve significant noise reduction and better aerodynamic performance of the axial fan by improving the sawtooth parameters of the fan and rearranging the sawtooth area. However, there are few application cases of the Metaverse in fluid machinery. The cases are limited to operating real equipment from a virtual environment and require the combination of virtual reality and DTT. The application effect still needs further verification.

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“…However, no one alyzed the control methods of dynamic wake for power maximization systematica paper's purpose is to judge the practicability of dynamic wake models and contr ods in the existing literature by analyzing and comparing, and to provide engine researchers with a practical introduction to wind farm dynamic wake models and methods by presenting a review of the progress. Digital twin (DT) technology is ered as a promising tool [49]; a virtual model can be used to make up for a lack sensors, and in this paper, a new DT framework for dynamic wake managemen posed for the first time to control wind farm power and fatigue damage, but the po of DT-based dynamic wake control needs to be studied further in future.…”

Section: Wake Effect and Wind Energymentioning

confidence: 99%

Progress on Offshore Wind Farm Dynamic Wake Management for Energy

Zhao

Xue

et al. 2022

JMSE

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The wake management of offshore wind farms (OWFs) mainly considers the wake effect. Wake effects commonly occur in offshore wind farms, which cause a 5–10% reduction in power production. Although there have been many studies on wake management, many methods are not accurate enough; for instance, look-up table and static wake model control methods do not consider the time-varying wake state. Dynamic wake management is based on the real-time dynamic wake, so it can increase the energy of the OWFs effectively. For OWFs, dynamic wake control is the main method of dynamic wake management. In this paper, the existing wake model and control progress are discussed, mainly emphasizing the dynamic wake model and the dynamic wake control method, solving the gap of the review for dynamic wake management. This paper presents a digital twins (DT) framework for power and fatigue damage for the first time.. The structure of this paper is as follows: (1) the mechanism of wind farm wake interference is described and then the dynamic wake model is reviewed and summarized; (2) different control methods are analyzed and the dynamic wake management strategies for different control methods are reviewed; (3) in order to solve the problems of dynamic wake detection and real-time effective control, the technology of DT is applied to the dynamic wake control of OWFs. This new DT frame has a promising application prospect in improving power and reducing fatigue damage.

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Digital Twins for Wind Energy Conversion Systems: A Literature Review of Potential Modelling Techniques Focused on Model Fidelity and Computational Load

Cited by 24 publications

References 158 publications

Predictive digital twin for offshore wind farms

Predictive digital twin for offshore wind farms

Application of Digital Twins and Metaverse in the Field of Fluid Machinery Pumps and Fans: A Review

Progress on Offshore Wind Farm Dynamic Wake Management for Energy

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