Adaptive NN Control of FESS with Constrained Full Order Error States

As more renewable energy resources are injected into the power system, it is an increasingly challenging task to retain the dependability and stability of the power grid. In this situation, installing an energy storage system (ESS) is an effective approach to improve the transient stability of the power system. Among the ESSs, the flywheel ESS (FESS) is an excellent source for the power system because of its several advantages. Therefore, in this paper, we propose an adaptive neural network (NN) error-constrained control algorithm that can accomplish the speedy charging of the FESS with guaranteed steady state performance and transient performance. First, as opposed to previous studies based on error transformation, we apply the error transformation to each subsystem of FESS, and the original constrained system is converted to a new unconstrained system. Because of this, we can guarantee predefined performance of each subsystem and further improve the system performance. Then, the new unconstrained system is stabilized by using the backstepping method and the unknown dynamics are tackled by NN approximators. Finally, we guarantee the boundness of all error signals via Lyapunov analysis and verify the effectiveness of theoretical results via numerical analysis.