Distributed Maximum Power Point Tracking Using Model Predictive Control for Photovoltaic Energy Harvesting Architectures Based on Cascaded Power Optimizers

Mismatching and partial shading in photovoltaic (PV) energy harvesting systems are the main causes for performance degradation and efficiency drop. A power electronic energy harvesting topology based on cascaded power optimizers that use distributed maximum power point tracking (MPPT) is believed to be one of the promising solutions to address these issues. In this scheme, each PV module is interfaced to the energy system through a separate dc/dc converter with maximum power point tracking capability. This paper presents application of the model predictive control technique to a distributed maximum power point tracking algorithm for maximizing the energy harvest performance of a cascaded power optimizer based system under dynamic weather conditions. The developed technique employs two control loops: a submodule maximum power point tracking model predictive control loop for each converter and a supervisory maximum power point tracking loop for power optimization of all cascaded PV modules. The provided experimental results confirm high energy capture, fast dynamic response, and negligible oscillations around MPP using the proposed method.