Performance Analysis of Model Predictive Control and Perturb & Observe MPPT for Solar PV Systems

The efficiency of solar PV systems is significantly affected by environmental variations, requiring robust Maximum Power Point Tracking (MPPT) techniques to ensure operational efficiency. Conventionally deployed methods, such as Perturb & Observe (P&O), typically suffer from steady-state oscillations and slow dynamic responses, leading to power losses. To address these challenges, this study investigates the potential of a novel hybrid predictive control strategy to enhance P&O-based MPPT performance. Specifically, a comparative analysis of hybrid Model Predictive Control (MPC) and (P&O (Duty Cycle) MPPT methods is reported under Standard Test Conditions (STC) for PV and irradiance step change conditions, using accurate Matlab/Simulink simulations. Under STC conditions, MPC demonstrated a significantly faster tracking speed and reduced steady-state oscillations; during step change analysis, MPC maintained an increased mean efficiency. The MPC framework also adds complexity to the design; if implemented correctly, it has promise for next-generation solar PV applications where real-time adaptability is crucial.