Distributed maximum power point tracking using model predictive control for solar photovoltaic applications

Mismatching and Partial Shading in Photovoltaic (PV) energy harvesting systems are the main causes for performance degradation and efficiency drop. Distributed Maximum Power Point Tracking (DMPPT) is one of the promising candidate solutions to address these issues. In DMPPT scheme, each PV module is interfaced to the energy system through a separate dc/dc converter with Maximum Power Point Tracking (MPPT) capability. This paper presents application of the Model Predictive Control (MPC) technique to a DMPPT system for maximizing the energy harvest performance under dynamic weather conditions. The continuous shift of the Maximum Power Point (MPP) due to dynamic weather conditions demands for a fast and adaptive control approach to guarantee the overall efficiency and stability of the DMPPT based PV system. This paper applies the MPC framework to a DMPPT system to regulate the duty ratio of the individual dc/dc converters to force the external impedance to approach the internal impedance by defining a cost function subject to minimization. The provided simulation and experimental results confirm the energy capture increase, fast dynamic response, and negligible oscillations around MPP using the proposed method.