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Wind Resource and Wind Power Generation Assessment for Education in Engineering

Authors: Estefania Artigao; Antonio Vigueras-Rodríguez; Andrés Honrubia-Escribano; Sergio Martín-Martínez; Emilio Gómez-Lázaro;

Wind Resource and Wind Power Generation Assessment for Education in Engineering

Abstract

This paper proposes a practical approach to assess wind energy resource and calculate annual energy production for use on university courses in engineering. To this end, two practical exercises were designed in the open-source software GNU Octave (compatible with MATLAB) using both synthetic and field data. The script used to generate the synthetic data as well as those created to develop the practical exercises are included for the benefit of other educational bodies. With the first exercise the students learn how to characterize the wind resource at the wind turbine hub height and adjust it to the Weibull distribution. Two examples are included in this exercise: one with an appropriate fit and another where the Weibull distribution does not fit properly to the generated data. Furthermore, in this exercise, field data (gathered with a LiDAR remote sensing device) is also used to calculate shear exponents for a proper characterisation of the wind profile. The second exercise consists of the calculation of the annual energy production of a wind power plant, where the students can assess the influence of different factors (wind speed, rotor diameter, rated power, etc.) in the project. The exercises proposed can easily be implemented through either in-class or online teaching modes.

Country
Spain
Keywords

Environmental effects of industries and plants, engineering education, TJ807-830, Energy education, sustainability, TD194-195, renewable energy, Engineering education, Renewable energy sources, Environmental sciences, Sustainability, energy education, GE1-350

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    citations
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    14
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
14
Top 10%
Top 10%
Top 10%
Green
gold