Nonlinear adaptive NeuroFuzzy feedback linearization based MPPT control schemes for photovoltaic system in microgrid.

Renewable energy resources connected to a single utility grid system require highly nonlinear control algorithms to maintain efficient operation concerning power output and stability under varying operating conditions. This research work presents a comparative analysis of different adaptive Feedback Linearization (FBL) embedded Full Recurrent Adaptive NeuroFuzzy (FRANF) control schemes for maximum power point tracking (MPPT) of PV subsystem tied to a smart microgrid hybrid power system (SMG-HPS). The proposed schemes are differentiated based on structure and mathematical functions used in FRANF embedded in the FBL model.

Consensus based SoC trajectory tracking control design for economic-dispatched distributed battery energy storage system.

The state-of-charge (SoC) of an energy storage system (ESS) should be kept in a certain safe range for ensuring its state-of-health (SoH) as well as higher efficiency. This procedure maximizes the power capacity of the ESSs all the times. Furthermore, economic load dispatch (ELD) is implemented to allocate power among various ESSs, with the aim of fully meeting the load demand and reducing the total operating cost.

Correction: Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station connected hybrid power system.

[This corrects the article DOI: 10.1371/journal.pone.

Indirect adaptive soft computing based wavelet-embedded control paradigms for WT/PV/SOFC in a grid/charging station connected hybrid power system.

This paper focuses on the indirect adaptive tracking control of renewable energy sources in a grid-connected hybrid power system. The renewable energy systems have low efficiency and intermittent nature due to unpredictable meteorological conditions. The domestic load and the conventional charging stations behave in an uncertain manner.