Roadmap to the multidisciplinary design analysis and optimisation of wind energy systems

Moreno, S. Sanchez Perez; Zaaijer, Michiel B.; Bottasso, Carlo L.; Dykes, Katherine; Merz, Karl; Réthore, Pierre-Élouan; Zahle, Frederik

doi:10.1088/1742-6596/753/6/062011

Cited by 19 publications

(13 citation statements)

References 19 publications

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“…Another issue that is not taken into account in this study is the influence of the turbines self-weight. As was found by Sieros et al (2012) the self-weight becomes more important for larger rotors. To accommodate for the added mass, a penalty could be added which should scale asR orR 3 for top head mass and static blade mass moment, respectively.…”

Section: Limitation Of the Study And Possible Improvementssupporting

confidence: 53%

“…From the inception of the wind energy industry, it has been a clear trend that rotor sizes have been increasing. However, as discussed in Sieros et al (2012), increasing the rotor size is not a clear way to decrease the cost of energy (CoE), since the rotor weight (closely related to rotor cost) will always scale with a larger exponent than the increase in power does. It is, therefore, argued that the lower CoE that has taken place is mostly due to improvements in technology.…”

Section: Introductionmentioning

confidence: 99%

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Optimal relationship between power and design-driving loads for wind turbine rotors using 1-D models

Loenbaek

Bak

Madsen³

et al. 2020

Wind Energ. Sci.

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Abstract. We investigate the optimal relationship between the aerodynamic power, thrust loading and size of a wind turbine rotor when its design is constrained by a static aerodynamic load. Based on 1-D axial momentum theory, the captured power P̃ for a uniformly loaded rotor can be expressed in terms of the rotor radius R and the rotor thrust coefficient CT. Common types of static design-driving load constraints (DDLCs), e.g., limits on the permissible root-bending moment or tip deflection, may be generalized into a form that also depends on CT and R. The developed model is based on simple relations and makes explorations of overall parameters possible in the early stage of the rotor design process. Using these relationships to maximize P̃ subject to a DDLC shows that operating the rotor at the Betz limit (maximum CP) does not lead to the highest power capture. Rather, it is possible to improve performance with a larger rotor radius and lower CT without violating the DDLC. As an example, a rotor design driven by a tip-deflection constraint may achieve 1.9 % extra power capture P̃ compared to the baseline (Betz limit) rotor. This method is extended to the optimization of rotors with respect to annual energy production (AEP), in which the thrust characteristics CT(V) need to be determined together with R. This results in a much higher relative potential for improvement since the constraint limit can be met over a larger range of wind speeds. For example, a relative gain in AEP of +5.7 % is possible for a rotor design constrained by tip deflections, compared to a rotor designed for optimal CP. The optimal solution for AEP leads to a thrust curve with three distinct operational regimes and so-called thrust clipping.

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Section: Limitation Of the Study And Possible Improvementssupporting

confidence: 53%

Section: Introductionmentioning

confidence: 99%

Optimal relationship between power and design-driving loads for wind turbine rotors using 1-D models

Loenbaek

Bak

Madsen³

et al. 2020

Wind Energ. Sci.

View full text Add to dashboard Cite

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“…This use case concerns the problem of defining a useful set of disciplines. Previous work in this area has been done by Sempreviva et al (2017).…”

Section: Computation Pathway Discoverymentioning

confidence: 99%

WESgraph: a graph database for the wind farm domain

Quaeghebeur

Perez-Moreno²,

Zaaijer

2020

Wind Energ. Sci.

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Abstract. The construction and management of a wind farm involve many disciplines. It is hard for a single designer or developer to keep an overview of all the relevant concepts, models, and tools. Nevertheless, this is needed when performing integrated modeling or analysis. To help researchers keep this overview, we have created WESgraph (the Wind Energy System graph), a knowledge base for the wind farm domain, implemented as a graph database. It currently contains 1222 concepts and 1725 relations between them. This paper presents the structure of this graph database – content stored in nodes and the relationships between them – as a foundational ontology, which classifies the domain's concepts. This foundational ontology partitions the domain in two: a part describing physical aspects and a part describing mathematical and computational aspects. This paper also discusses a number of generally difficult cases that exist when adding content to such a knowledge base. This paper furthermore discusses the potential applications of WESgraph and illustrates its use for computation pathway discovery – the application that triggered its creation. It also contains a description of our practical experience with its design and use as well as our thoughts about the community use and management of this tool.

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“…Due to the complexity of wind energy systems and the large number of disciplines involved in their design, practitioners have recently started applying MDAO in this domain 1 . We have applied the proposed guideline to select the optimal MDAO workflow for an offshore wind farm layout optimisation problem (WFLOP).…”

Section: Iva Use Case and Problem Formulationmentioning

confidence: 99%