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

Mohamed I. Daoud1, Ayman S. Abdel-Khalik2, A. Elserougi2, A. Massoud1, S. Ahmed3, and Nabil H. Abbasy2
1. Qatar University, Qatar, mdaoud@qu.edu.qa
2. Alexandria University, Egypt
3. Texas A&M University at Qatar, Qatar
Abstract—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.

Cite: Mohamed I. Daoud, Ayman S. Abdel-Khalik, A. Elserougi, A. Massoud, S. Ahmed, and Nabil H. Abbasy, "An Artificial Neural Network Based Power Control strategy of Low-Speed Induction Machine Flywheel Energy Storage System," Journal of Advances in Information Technology, Vol. 4, No. 2, pp. 61-68, May, 2013.doi:10.4304/jait.4.2.61-68
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