Enhanced Photovoltaic Systems Performance: Anti-Windup PI Controller in ANN-Based ARV MPPT Method
Musa Yılmaz, Reşat Çelikel, Ahmet Gündoğdu
Abstract
Photovoltaic (PV) panels exhibit a non-linear current-voltage characteristic with a Maximum Power Point (MPP) that varies due to environmental factors such as solar radiation and ambient temperature. In this study, an Artificial Neural Network (ANN)-based MPPT method, called the ANN-based Adaptive Reference Voltage (ARV) method, is proposed to determine the optimal operating point of the PV panel. The ANN-based ARV method is a voltage-controlled approach that can adapt to changing atmospheric conditions. The performance of the proposed method is evaluated using both a normal Proportional-Integral (PI) controller and an anti-windup PI controller. Comparative analysis is conducted with the widely used Perturb and Observe (P&O) and Incremental Conductance (INC) methods in the MATLAB/Simulink environment, considering three different atmospheric scenarios with varying radiation levels according to EN50530 standards. The proposed method demonstrates superior efficiency with overall results of 99.4%, 95.9%, and 96% in scenario 1, scenario 2, and scenario 3, respectively. Particularly, the proposed method exhibits notable superiority in rapidly changing atmospheric conditions.