Turkish Journal of Electrical Engineering and Computer Sciences




The immense emergence of plug-in hybrid electric vehicles (PHEVs) is envisioned in the future. The rapid proliferation of PHEVs and their charging triggers intense surges in the load during load peak hours. A sophisticated controlled charging station is developed for PHEVs to alleviate grid load during peak demand hours. A novel feedback linearization embedded full recurrent adaptive NeuroFuzzy Legendre wavelet control (FBL-FRANF-Leg-WC) technique is employed to control the charging of PHEVs. The antecedent part of the NeuroFuzzy framework is based on recurrent Gaussian membership function while the consequent part comprises of recurrent Legendre wavelet. The charging station is integrated into a grid-connected microgrid hybrid power system. The charging station consists of five different PHEVs with seven different modes of operation. The performance of the control scheme is tested for various power quality and power system stability parameters. The effectiveness of the suggested control scheme is validated through simulation results by comparing with adaptive NeuroFuzzy, adaptive PID, and conventional PID control scheme.


Smart microgrid hybrid power system (SMG-HPS), plug-in hybrid electrical vehicle (PHEV), maximum power point tracking (MPPT), charging station (CS), feedback linearization (FBL), NeurFuzzy (NF), recurrent, legendre wavelet

First Page


Last Page