Optimizing PV power production by incremental conductance and model predictive control maximum power point tracking technique

Abstract

Photovoltaic (PV) energy sources play a crucial role in renewable energy applications, but their energy conversion efficiency remains relatively low. Despite the vital role played by Maximum Power Point Tracking (MPPT) algorithms for optimizing power production from PV systems, they are associated with delay in response time, which potentially leads to insufficient energy harvesting. Addressing this challenge requires collaborative efforts from researchers. This study improved the dynamic capability of maximum power tracking technique by combining Incremental Conductance (INC) MPPT algorithm and Model Predictive Control (MPC) algorithm. The improved method has achieved a tracking efficiency of 98.2% at 25⁰C, 98.8% at 35⁰C and 98.5% at 25⁰C under varying temperature and constant irradiance condition of 1000W/m².This indicate that the developed method is more effective in optimizing the MPPT technique. When compared with Perturb and Observe-MPC method, the developed method has a faster dynamic capability, better tracking performance and is more effective in maximizing the power output of PV system.