Field spectra binning for energy production calculations and multijunction solar cell design

García Vara, Iván; Mcmahon, William E.; Habte, Aron; Geisz, John F.; Steiner, Myles A.; Sengupta, Manajit y Friedman, Daniel J. (2015). Field spectra binning for energy production calculations and multijunction solar cell design. En: "42nd Photovoltaic Specialist Conference (PVSC 2015)", 14/06/2015 - 19/06/2015, New Orleans, LA, EE.UU. pp. 1-3. https://doi.org/10.1109/PVSC.2015.7356207.

Descripción

Título: Field spectra binning for energy production calculations and multijunction solar cell design
Autor/es:
  • García Vara, Iván
  • Mcmahon, William E.
  • Habte, Aron
  • Geisz, John F.
  • Steiner, Myles A.
  • Sengupta, Manajit
  • Friedman, Daniel J.
Tipo de Documento: Ponencia en Congreso o Jornada (Artículo)
Título del Evento: 42nd Photovoltaic Specialist Conference (PVSC 2015)
Fechas del Evento: 14/06/2015 - 19/06/2015
Lugar del Evento: New Orleans, LA, EE.UU
Título del Libro: 42nd Photovoltaic Specialist Conference (PVSC 2015)
Fecha: 2015
Materias:
Escuela: E.T.S.I. Telecomunicación (UPM)
Departamento: Electrónica Física
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

Annual spectra sets must be used for accurate energy production prediction and multijunction solar cell design for maximum energy production at a specific site. These spectra sets contain a large quantity of data that is cumbersome to manage during solar cell design calculations and impractical to reproduce in solar simulators for indoor energy production measurements. However, it should be possible to bin together spectra with similar spectral contents, and then use this reduced set with little loss of accuracy. We present two binning algorithms which judiciously bin together similar spectra to create a much smaller "proxy" set, for which the total measurement time, energy production calculation and solar cell optimization decreases to a matter of seconds. These algorithms are assessed against their accuracy in representing the whole spectra sets for solar cell design and energy production prediction. We find that a set of just five spectra fulfills this requirement. In addition, the sets of proxy spectra act as "fingerprints" of specific sites, and provide an efficient and effective way to understand how cell design and performance vary from site to site. Furthermore, the process of reducing a full data set to a few proxy spectra can help assess the quality of the dataset for multijunction applications, and contribute to improvements to the datasets and data collection methods.

Más información

ID de Registro: 42022
Identificador DC: http://oa.upm.es/42022/
Identificador OAI: oai:oa.upm.es:42022
Identificador DOI: 10.1109/PVSC.2015.7356207
Depositado por: Memoria Investigacion
Depositado el: 27 Jul 2016 17:49
Ultima Modificación: 27 Jul 2016 17:49
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