Application of adaptive Lyapunov-based UPFC supplementary controller by neural network algorithm in multi-machine power system

In this paper, a new adaptive unified power flow controller (UPFC) based on the Lyapunov method and neural network structure is presented. The corresponding energy function is derived for the single machine infinitive bus system with classic generator model representation. Damping control strategy to improve transient behavior of the system is determined by considering the dynamic modeling of the UPFC. The Lyapunov-based controller is extended to interconnected power system by considering the two-machine equivalent model and the center of inertia concept. The recurrent neural network (RNN) with back propagation algorithm is also used to overcome the uncertainty issues and also to consider the more detailed power system. The designed Lyapunov-RNN-based controller is applied to the interconnected power system between the Esfahan-Yazd region transmission network in Iran power system. The performance of the proposed controller is compared with other different controllers by applying some disturbances in the system. Finally, simulation results are presented and the effectiveness of the proposed method for power system stability enhancement is discussed as well.