Distributed Maximum Power Point Tracking Using Model Predictive Control for Photovoltaic Energy Harvesting Architectures Based on Cascaded Power Optimizers - 2017

PROJECT TITLE :

Distributed Maximum Power Point Tracking Using Model Predictive Control for Photovoltaic Energy Harvesting Architectures Based on Cascaded Power Optimizers - 2017

ABSTRACT:

Mismatching and partial shading in photovoltaic (PV) energy harvesting systems are the most causes for performance degradation and potency drop. A Power Electronic energy harvesting topology based on cascaded power optimizers that use distributed maximum power purpose tracking (MPPT) is believed to be one of the promising solutions to handle these problems. In this theme, each PV module is interfaced to the energy system through a separate dc/dc converter with maximum power purpose 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 beneath dynamic weather. The developed technique employs 2 control loops: a submodule most power purpose tracking model predictive control loop for every 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, quick dynamic response, and negligible oscillations around MPP using the proposed technique.

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