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 primarily based on cascaded power optimizers that use distributed most power point tracking (MPPT) is believed to be one of the promising solutions to handle these problems. During this scheme, each PV module is interfaced to the energy system through a separate dc/dc converter with most power point tracking capability. This paper presents application of the model predictive management technique to a distributed most power purpose tracking algorithm for maximizing the energy harvest performance of a cascaded power optimizer based system underneath dynamic atmospheric condition. The developed technique employs two management loops: a submodule maximum power purpose tracking model predictive management loop for each converter and a supervisory maximum power point tracking loop for power optimization of all cascaded PV modules. The provided experimental results ensure high energy capture, fast dynamic response, and negligible oscillations around MPP using the proposed methodology.

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