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Browsing by Author "University of Minnesota St. Anthony Falls Laboratory"

Now showing 1 - 4 of 4
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    Rich Data for Wind Turbine Power Performance Analysis
    (2019-07-31) Davison, Brian; B.Davison@napier.ac.uk; Davison, Brian; University of Minnesota St. Anthony Falls Laboratory
    The post-installation verification of wind turbine performance is an essential part of a wind energy project. Data collected from meteorological instruments and from the turbine is analysed to produce an estimate of the annual energy production (AEP) which is compared against expectations. However, turbine warranties can impose very strict data filtering criteria which can lead to high rates of data loss. As a consequence, measurement campaigns may last longer than expected and incur additional costs for the development. This project aims to investigate the extent of the problem and the potential of alternative data filtering strategies with respect to data loss, AEP estimates and the dispersion of points in the power curve scatter plot. In doing so, it targets a wide range of meteorological parameters with theoretical relationships to wind turbine power production with particular interest in those not accounted for in the current standard. The identification of viable filtering strategies with lower data loss would provide significant benefits to wind energy development projects in terms of greater control over timescales and reduced costs. Data from a sample of power performance tests is analysed to explore the range and severity of the problem of data loss. It confirms the wide variation in warranty conditions, demonstrates the extent and likelihood of data losses and quantifies the financial implications within the limits of commercial sensitivity. When indirect costs are taken into consideration, the impact of extended measurement campaigns can theoretically reach tens of millions of pounds. A new, high-fidelity dataset is then compiled so that the effects of alternative filtering strategies can be examined. The dataset covers the whole of 2017 and consists of over 700 parameters of which 74 are selected for investigation here. The eFAST method of global sensitivity analysis is used in combination with correlation analysis to reduce this number to 11 parameters which are then used to define alternative filtering criteria. Similar AEP estimates are obtained by application of conventional and experimental criteria to the research dataset. In the case of the experimental filters, however, the data loss was 11% compared to 63% data loss with conventional filters. Conventional filters were also shown to increase the dispersion in the power curve scatter plot by over 10%, while dispersion did not increase significantly with the experimental filters.
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    Supporting Data for Mechanistic study of shoaling effect on momentum transfer between turbulent flow and traveling wave using large-eddy simulation
    (2019-11-04) Hao, Xuanting; Cao, Tao; Shen, Lian; haoxx081@umn.edu; Xuanting, Hao; University of Minnesota St. Anthony Falls Laboratory
    The data are results of the large-eddy simulation of wind turbulence over monochromatic waves propagating in coastal and oceanic waters, described in the paper by Hao, Cao, and Shen "Mechanistic study of shoaling effect on momentum transfer between turbulent flow and traveling wave using large-eddy simulation " (published in Physical Review Fluids).
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    Supporting data for Study of The Fundamental Dynamics of Water Wave Effects on Turbulence for Environmental Applications
    (2021-08-25) Shen, Lian; Xuan, Anqing; Deng, Bingqing; xuanx004@umn.edu; Xuan, Anqing; University of Minnesota St. Anthony Falls Laboratory
    The data are results of the numerical simulation of the wave-phase-resolved Langmuir turbulence, described in the papers by Xuan, Deng and Shen. The data are shared as required by the sponsoring projects.
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    Supporting data for: Numerical Study of Near-Surface Jet in the Atmospheric Surface Layer over Oceanic Fronts
    (2020-04-17) Deng, Bing-Qing; Zhao, Ming-Xiang; Wang, Qing; Shen, Lian; bdeng@umn.edu; Deng, Bing-Qing; University of Minnesota St. Anthony Falls Laboratory
    The data are results of the numerical simulation of the near-surface jet in the atmospheric surface layer across oceanic fronts, described in the paper by Deng, Zhao, Wang, and Shen (submitted to JGR-Atmosphere). The data are shared to follow the common Enabling FAIR data Project guidelines as required by the journal.