An Artificial Neural Network Based Power Control strategy of Low-Speed Induction Machine Flywheel Energy Storage System

This study introduces a power control strategy of a flywheel energy storage system (FESS) based on an artificial neural network (ANN) as a current decoupling network to charge/discharge the flywheel for grid connected applications such as grid frequency support/control, power conditioning and UPS applications. The proposed system is a large-capacity low-speed FESS based on a field oriented controlled (FOC) squirrel cage induction machine. The controller is designed to avoid machine overloading while the flywheel is charged/discharged. Additionally, it avoids using the required outer power loop or a hysteresis power controller, hence, simplifies the overall control algorithm. The validity of the developed control system is investigated via computer simulations using MATLAB/Simulink as well as experimental results. The proposed system is also compared with conventional power control strategy with an additional outer power control loop to highlight their equivalence. Index Terms-Flywheel energy storage, artificial neural network, instantaneous power control, indirect field orientation.