Abstract
This paper investigates the use of an adaptive power system stabilizer (PSS) for improving the dynamic stability of a power system. Adaptive network based fuzzy inference systems (ANFIS) and the second version of non-dominated sorting genetic algorithms (NSGA-II) is employed to select the optimal parameters of the controller for different loading conditions. Firstly genetic algorithms are used to tune stabilizer parameters on a wide range of loading conditions to create a data base. Two eigenvalue-based objective functions are considered to place the closed-loop system eigenvalues in the D-shape sector. Then, the relationship between these operating points and the corresponding stabilizer parameters is learned by the ANFIS. The proposed stabilizer has been tested by performing non linear simulations and eigenvalue analysis using single machine infinite bus (SMIB) model. The results show the effectiveness and the robustness of the proposed stabilizer to provide efficient damping in real-time.