Maximum power point tracking mppt and power flow management of solar pv generation system using advanced techniques

Abstract

Nowadays the use of renewable energy sources for electricity newlinegeneration has increased. Photovoltaic (PV) is the important environmental newlinefriendly renewable energy source. The main demerits of the photovoltaic newlinemodule are low energy conversion and high fabrication cost, due to their linear newlineand temperature characteristics. Converters are utilized for boosting the output newlineof the PV system. This research focuses on optimally maximize the PV module newlineoutput power by tracking continuously the maximum power point (MPP), newlinemaintaining the power flow of the system at source and load side, satisfy the newlineload demand of the system. Based on temperature and irradiation the MPP of newlinethe PV is varying. Hence, the Maximum power point tracking (MPPT) newlineapproaches are utilized to extract the maximum power. Perturb and observation newlinealgorithm is a MPPT approach which is used to adjust the MPP and boost the newlinevoltage of the system. Power flow management provides cost reduction for newlinegeneration of active power and reduces the loss of the system. newlineThis research work proposed an efficient hybrid approach for extract newlinethe maximum power and power flow management of the PV system. The newlineproposed hybrid approach is the combined execution of Quasi Oppositional newlineChaotic Grey Wolf Optimizer (QOCGWO) with the Random Forest Algorithm newline(RFA), hence named as QOCGWO-RFA approach. The GWO has some newlinedrawbacks like weak solutions during optimization, low solving accuracy, bad newline