A Neural Network Integration of Virtual Synchronous Motor-Based EV Charging Stations Control Performance and Plant Stability Enhancement

Control techniques for neural-network-based charging stations (CSs) are attracting attention worldwide. This popularity is due to the emergent need for alternative intelligent and adaptive control solutions for attaining a CS with stabilized power transfer and voltage control at the point of common coupling. This paper demonstrates novel neural-network-based improved virtual synchronous motor (NN-i-VSM) control through the mechanism of the charging voltage feedback in conjunction with a trained neural network model to adaptively produce field excitation (MN) that mimics a virtual flux model. The MN adaptively generates an electromotive force based on the trained NN output to control the rectifying converter response of the CS for power quality enhancement during multiple-CS operation. Simulation results in the scenario of multiple CSs at 750 kW (5 × 150 kW) with varying capacities showed significant improvement in voltage variable tracking capacity of up to 500 V as well as power response overshot reduction and grid voltage response tracking improvement compared with an i-VSM-based CS model. A comprehensive CS efficiency assessment and plant stability analysis, including Bode plot evaluation, further confirmed the superior dynamic response performance and robustness of the NN-i-VSM model over the i-VSM model. The proposed model offers scalable applicability in smart mobility and wireless CS integration, signifying a new control advancement for future generations of multiple-grid-friendly charging infrastructure for penetration of batteries at varying capacities.