Small-Signal Modeling and Parameter Extraction Method for Photovoltaic Cell Integration in Indoor Visible Light Communication Systems

Authors

  • Diego Mattos UNIPAMPA - Federal University of Pampa - Brazil
  • Vitoria Monteiro UNIPAMPA - Federal University of Pampa - Brazil
  • Paulo César de Aguirre UNIPAMPA - Federal University of Pampa - Brazil
  • Lucas Severo Aeronautics Institute of Technology - ITA - Brazil
  • Alessandro Girardi UNIPAMPA - Federal University of Pampa - Brazil

DOI:

https://doi.org/10.29292/jics.v19i1.763

Keywords:

PV cell, Modeling, VLC

Abstract

Photovoltaic (PV) cells are being adopted as a viable and cost-effective option for implementing receivers within Visible Light Communication (VLC) systems, primarily in indoor environments. Accurately estimating the generated current and voltage of the PV cell based on incident light is crucial when designing VLC systems.
For this assessment, the 1D2R electrical equivalent model, which incorporates a diode and two resistors, is employed.
In AC small signal analysis, the diode is substituted by its dynamic counterpart, which comprises a dynamic resistance in parallel with an equivalent capacitance. This study introduces an approach to measure and characterize the small-signal parameters of a PV cell operating at the maximum power point (MPP), open circuit (OC), and short circuit (SC) bias points. This is achieved through a closed-loop frequency response system, calibrated to encompass illuminance levels ranging from 50 to 500 lux.
The procedure for estimating the AC response of the PV cell is outlined, and the outcomes are subsequently employed in an analytical parameter extraction methodology. Experimental results from a 20 x 40 mm PV cell reveal that MPP represents the least favorable bias point in terms of bandwidth, whereas the SC bias point exhibits the most favorable performance. This observation validates the hypothesis that the optimal bias point for energy harvesting in PV cells is the worst bias point for communication purposes.

Downloads

Published

2024-03-15

Issue

Section

Selected Papers from SIM 2023. Guest Editor: Mateus Grellert (UFRGS - Brazil)