Mahmoud A. YOUNIS, Tamer KHATIB, Mushtaq NAJEEB, A Mohd ARIFFIN
الملخص العربي
This paper presents an improved maximum power point tracking (MPPT) controller for PV systems. An Artificial Neural Network and the
classical P&O algorithm were employed to achieve this objective. MATLAB models for a neural network, PV module, and the classical P&O
algorithm are developed. However, the developed MPPT uses the ANN to predict the optimum voltage of the PV system in order to extract the
maximum power point (MPP). The developed ANN has a feedback propagation configuration and it has four inputs which are solar radiation,
ambient temperature, and the temperature coefficients of Isc and Voc of the modeled PV module. Meanwhile, the optimum voltage of the PV system
is the output of the developed ANN. Based on the results; the response of the proposed MPPT controller is faster than the classical P&O algorithm.
Moreover, the average tracking efficiency of the developed algorithm was 95.51% as compared to 85.99% of the classical P&O algorithm. Such
developed controller increases the conversion efficiency of a PV system.
مشتاق نجيب أحمد | Mushtaq Najeeb Ahmed | 9803