Adaptive virtual inertia controller based on machine learning for superconducting magnetic energy storage for dynamic response enhanced

The goal of this paper was to create an adaptive virtual inertia controller (VIC) for superconducting magnetic energy storage (SMES). An is designed using extreme learning machine (ELM). test system a 25-bus interconnected Java Indonesian power grid. Time domain simulation used evaluate the effectiveness proposed method. To simulate case study, MATLAB/Simulink environment used. According results, can be make adaptable variation. It has also been discovered that designing based on not only makes VIC any change in but provides better dynamics performance when compared other scenarios (the overshoot value less than -5×10-5).