In this paper, an auxiliary controller of static var compensator (STATCOM) stabilizer is designed to enhance transient stability and damping power network fluctuations. At first, by analyzing the eigenvalues, the oscillation modes with low damping and undamped oscillations, especially the interarea oscillation modes are determined in the network under study. By performing observability analysis, the best controller input signal corresponding to each critical oscillation mode is selected and applied to the controller. To achieve the optimal performance of the STATCOM controller in different operating conditions and different disturbances, the adaptive critic design (ACD) method has been used as a combination of the optimization function that can learn the neural network. ACD neural network includes model neural networks for dynamic learning, critic network for optimization and action network which is designed under the title of the controller. To increase the damping and improve the dynamic performance of the network, ANFIS has been used in the action section. In the end, the dynamic performance of the network is designed with a wide-area damping controller, and the particle swarm optimization (PSO)-based controller and the common PI controller are compared.
