Hybrid Incremental Conductance–Model Predictive Control for Maximum Power Point Tracking Under Varying Irradiance and Temperature Conditions

Abstract

Photovoltaic systems experience variations in irradiance and temperature that continuously shift the maximum power point, making effective MPPT essential for maximizing energy extraction. Conventional methods such as Perturb and Observe and Incremental Conductance are simple to implement but may suffer from slow convergence, oscillations, and reduced accuracy under rapidly changing conditions. Model Predictive Control offers faster dynamic response but at the cost of higher computational requirements. This study developed a hybrid Incremental Conductance–Model Predictive Control algorithm for photovoltaic MPPT. The Incremental Conductance stage generated the reference voltage, while the finite-control-set predictive controller selected the switching state of the boost converter that minimized the voltage-tracking error. The controller was implemented in MATLAB/Simulink and evaluated at irradiance levels of 1000, 800 and 500 W/m², with cell temperatures ranging from 25°C to 35°C. Its performance was compared with that of a conventional Perturb and Observe–Model Predictive Control method. The proposed controller achieved a tracking efficiency of 98.8%, compared with 96.9% for the conventional controller. In addition, the overall settling time decreased from 100 ms to 70 ms, while the steady-state power oscillation was reduced from 8.5 W to 2.3 W. Similarly, the controller was observed to have produced lower overshoot and a faster rise time. These simulation results show that the hybrid IN-MPC method improved energy extraction, transient response and steady-state stability under the investigated operating conditions. However, this study recommends both eexperimental and hardware-based studies to confirm its real-time performance.