Simulation study of a SSSC-based neuro-fuzzy controller for improvement of transient stability in a three-machine power system

In this article, computationally simple and accurate expert system, i.e., neuro-fuzzy system based on the artificial neural network (ANN) is applied to design a static synchronous series compensator (SSSC)-based controller for improvement of transient stability in a three-machine power system. The proposed neuro-fuzzy controller combines the advantages of fuzzy controller as well as quick response and adaptability nature of ANN. The neuro-fuzzy structures were trained using the generated database of fuzzy controller for SSSC. The results prove that the proposed SSSC-based neuro-fuzzy controller is found to be robust to fault location and change in operating conditions. A thorough comparison with the conventional lead-lag controller is carried out, taking into account the results of previous publications. The SSSC-based neuro-fuzzy controller output provides promising results in terms of accuracy and computation time. Finally, conclusions are duly drawn.