Intelligent Control Strategy to Enhance Power Smoothing of Renewable based Microgrid with Hybrid Energy Storage
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Abstract
A stand-alone renewable based microgrid (MG) performance with a hybrid energy storage system has been examined in this work. Stand-alone MG system mainly consists of a solar photovoltaic (PV) and permanent magnet synchronous generator (PMSG) based wind system. The hybrid energy storage system is based on Ni-Metal- Hydride (NiMH) battery and a supercapacitor (SC). The paper's primary goal is to propose an artificial neural network (ANN) based control strategy for charging/discharging control of Ni-Metal- Hydride battery & supercapacitor. The proposed maximum power tracking techniques (MPPT) include perturb and observe (P& O) algorithm for solar PV system while optimum torque (OT) MPPT for PMSG based wind turbine. The ANN-based control mechanism can maintain the DC bus voltage constant and trigger the supercapacitor to limit the battery current when the battery charging/ discharging current reached its threshold value. The proposed model responds quickly to intermittent nature PV-wind power generation or load power variation.
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