IJCAS

Maximizing the efficiency of photovoltaic (PV) systems relies heavily on employing efficient maximum power point tracking (MPPT) algorithms. This research focuses on the advancement of enhanced MPPT algorithms capable of achieving the maximum power point (MPP) under different climatic profiles. This paper proposes an adapted perturb and observe-based model predictive control (APO-MPC) strategy to validate the effectiveness of PV systems under three climatic situations. The APO algorithm incorporates variable step sizes to compute reference currents to reduce oscillations while maintaining a steady state in output power. The APO-MPC efficiently tracks and stabilizes output power by predicting future states using reference current and minimizing the cost function. This eliminated the necessity for expensive sensing and communication equipment and networks designed for directly measuring variations in solar irradiation. The computational burden of an algorithm is reduced using a simplified mathematical model of a boost converter and a one-step prediction approach. The PV panel and boost converter are modeled to get appropriate parameters for implementing the proposed algorithm. The system undergoes simulations using the MATLAB/Simulink environment, and multiple test cases are conducted under constant, rapid, and linearly changing irradiances. The outcomes demonstrate that the proposed APO-MPC MPPT algorithm outperforms APO, Kalman filter-based MPC (KMF-MPC), and other existing strategies in terms of stability, transient response time, overshoots, steady-state oscillations, and follow of reference trajectory under dynamic weather conditions.

Keywords: Converters, maximum power point tracking, model predictive control, nonlinear system, optimal control, photovoltaic systems.