Evaluating machine learning models comprehensively for predicting maximum power from photovoltaic systems.

This paper presents a machine learning (ML) model designed to track the maximum power point of standalone Photovoltaic (PV) systems. Due to the nonlinear nature of power generation in PV systems, influenced by fluctuating weather conditions, managing this nonlinear data effectively remains a challenge. As a result, the use of ML techniques to optimize PV systems at their MPP is highly beneficial.

Optimizing power sharing accuracy in low voltage DC microgrids considering mismatched line resistances.

The main difficulties facing the operation of parallel converters in DC microgrids (DCMGs) are load sharing, circulation current, and bus voltage regulation. A droop controller is commonly used to control current sharing among parallel DC-DC converters due to its simplicity. However, the values of droop parameters impact both bus voltage regulation and the error in current sharing among converters.