Experimental evaluation of adaptive maximum power point tracking for a standalone photovoltaic system

Research output: Contribution to journalArticlepeer-review

Abstract

The adaptability of maximum power point tracking (MPPT) of a solar PV system is important for integration to a microgrid. Depending on what fixed step-size the MPPT controller implements, there is an impact on settling time to reach the maximum power point (MPP) and the steady state operation for conventional tracking techniques. This paper presents experimental results of an adaptive tracking technique based on Perturb and Observe (P&O) and Incremental Conductance (IC) for standalone Photovoltaic (PV) systems under uniform irradiance and partial shading conditions. Analysis and verification of measured and MATLAB/Simulink simulation results have been carried out. The adaptive tracking technique splits the operational region of the solar PV’s power–voltage characteristic curve into four and six operational sectors to understand the MPP response and stability of the technique. By implementing more step-sizes at sector locations based on the distance of the sector from the MPP, the challenges associated with fixed step-size is improved on.The measured and simulation results clearly indicate that the proposed system tracks MPP faster and displays better steady state operation than conventional system. The proposed system’s tracking efficiency is over 10% greater than the conventional system for all techniques. The proposed system has been under partial shading condition has been and it outperforms other techniques with the GMPP achieved in 0.9 s which is better than conventional techniques.
Original languageEnglish
Pages (from-to)(In-press)
Number of pages19
JournalEnergy systems
Volume(In-press)
Early online date18 Apr 2021
DOIs
Publication statusE-pub ahead of print - 18 Apr 2021

Keywords

  • Solar
  • Perturb and Observe (P&O)
  • Photovoltaic (PV)
  • Incremental Conductance (IC)
  • MPPT

ASJC Scopus subject areas

  • Energy(all)
  • Economics and Econometrics
  • Modelling and Simulation

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