Hydrogen Fuel Cell‐Powered Synchronized Induction Motor Drive With ANN‐Based Fuel Flow Optimization for Off‐Grid Energy Systems

ABSTRACT Hydrogen‐based power conversion systems have attracted increasing attention for off‐grid and autonomous energy applications because, unlike solar and wind resources, hydrogen fuel cells can deliver continuous electrical power independent of weather conditions. In parallel, synchronized induction motors offer an attractive compromise between the high starting torque of wound‐rotor induction machines and the constant‐speed capability of synchronous operation. This study presents an integrated simulation model of a hydrogen fuel cell‐powered synchronized induction motor drive in which the classical fuel‐flow determination stage is replaced by an artificial neural network (ANN)‐based estimator. The motor is represented in the phase‐variable domain, synchronization is achieved by the Danielson rotor connection, and speed regulation is performed by a vector control‐based drive structure. All principal modeling assumptions, controller settings, ANN training parameters, and operating scenarios are stated explicitly to satisfy reproducibility requirements. The simulation scenario includes asynchronous starting, synchronization at t = 2 s, a simultaneous speed and load transition at t = 4 s (to 110 rad/s and 0.4 Nm), and a second transition at t = 6 s (to 135 rad/s and 0.2 Nm). The results confirm that the ANN‐based fuel‐flow estimator reproduces the required hydrogen demand while preserving stable interaction between the fuel cell and the motor drive under all three operating regimes. The revised study demonstrates that ANN‐assisted hydrogen flow management can be incorporated into a PEMFC‐powered synchronized induction motor system without compromising electromechanical stability while substantially strengthening the scientific scope and originality of the work. This makes the system a promising candidate for hydrogen‐supported microgrids and off‐grid energy systems.