@inproceedings{d7aefbcbdcc642b9a256ba095424fa5f,
title = "Maximum power point tracking of photovoltaic systems using sensorless current-based model predictive control",
abstract = "Variability of the solar resource necessitates that Maximum Power Point Tracking (MPPT) techniques be used in photovoltaic (PV) systems to ensure maximum electrical energy is harvested. This paper presents a MPPT algorithm using Model Predictive Control (MPC) that does not require the use of current sensors. The main contribution is the use of the model based predictive control (MPC-MPPT) to eliminate the current sensor that is usually required in the perturb and observe (P\&O) MPPT technique. By predicting and controlling the future PV system operation in the time horizon, the proposed method is an elegant, embedded controller that has faster response than the conventional P\&O technique under rapidly changing atmospheric conditions and without requiring expensive sensing and communications equipment and networks to directly measure solar insolation changes. Real time simulations run on a dSpace DS1007 platform compare of the proposed sensorless current MPC-MPPT (SC MPC-MPPT) technique to the full sensor version.",
author = "Morcos Metry and Shadmand, \{Mohammad B.\} and Yushan Liu and Balog, \{Robert S.\} and Rub, \{Haitham Abu\}",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 7th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2015 ; Conference date: 20-09-2015 Through 24-09-2015",
year = "2015",
month = oct,
day = "27",
doi = "10.1109/ECCE.2015.7310588",
language = "English",
series = "2015 IEEE Energy Conversion Congress and Exposition, ECCE 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6635--6641",
booktitle = "2015 IEEE Energy Conversion Congress and Exposition, ECCE 2015",
address = "United States",
}