Authors: Hachimenum Nyebuchi Amadi, Henry Okechukwu Williams, Richeal Chinaeche Ijeoma
Abstract: The rapid integration of electric vehicle (EV) fast charging stations in DC micro-grids has introduced significant power quality challenges, particularly harmonic current distortion at the point of common coupling (PCC). In this study, a DC microgrid integrating photovoltaic (PV) generation, battery energy storage systems (BESS), and a Level-3 EV fast charging station was modeled in MATLAB/Simulink to examine the effect of harmonic distortion and evaluate mitigation using an Artificial Neural Network (ANN)-controlled Static Synchronous Compensator (STATCOM). Base case simulation results revealed that the EV fast charging station injected excessive harmonic distortion into the network, with dominant odd harmonics at the 11th and 13th orders, leading to a total harmonic distortion (THD) of 14.05%. This value significantly exceeds the IEEE 519-2022 standard limit of 8% for medium-voltage systems. Following the installation of an ANN-tuned STATCOM at the PCC, the harmonic distortion was substantially mitigated, reducing the 11th and 13th orders to 0.01% and 0.15% respectively. Consequently, the total harmonic distortion was minimized to 1.23%, achieving a 91.24% reduction and ensuring full compliance with IEEE standards. Furthermore, the ANN controller demonstrated excellent training performance with a best validation mean square error of 0.0034611 at epoch 20 and a regression correlation coefficient of R = 0.9879, validating its accuracy and robustness. These findings confirm that ANN-controlled STATCOM provides an effective and intelligent solution for enhancing power quality and system stability in DC micro-grids with EV fast charging integration.
DOI: http://doi.org/
