Asima Syed∗ and Mairaj ud din Mufti∗
Superconducting magnetic energy storage, automatic generationcontrol, adaptive one-step ahead predictive control, frequency devi-ations, tie-line power deviations
For dynamic performance reinforcement of modern power systems, the application of energy storage systems such as superconducting magnetic energy storage (SMES) is imperative. In this paper, a non-linear adaptive one-step ahead predictive control based on neural networks is proposed for a small rating SMES unit integrated in a three-area power system. The optimal power command dispensed by the proposed discrete controller effectively governs the operational constraints of the SMES system. The limits on converter power are impelled to vary as the SMES current reaches its threshold values instead of adopting the constant limits to evade the sudden outage of the storage device from the system. Furthermore, energy level constraints of SMES are contemplated by developing a control-oriented second-order system that translates the hardware constraints of coil current into modified power constraints. The simulation results performed on the three-area power system substantiates the potency of the proposed control strategy.
Important Links:
Go Back
