Intelligent dual input power system stabilizer

Abstract This paper presents a new approach for real-time tuning of a dual input power system stabilizer using neuro-fuzzy system (NFS). Intelligent dual input power system stabilizer (IDIPSS) comprises of NFS and conventional dual input power system stabilizer. The NFS is a fuzzy inference system implemented in the framework of multi-layered feed forward adaptive network. NFS network is trained using hybrid training algorithm for real-time tuning of the dual input power system stabilizer. The generator real power ( P e ), reactive power ( Q e ), and terminal voltage ( V t ) characterizing the operating condition are input signals to the network while optimum DIPSS parameters K S1 and T 1 are the outputs. Investigations have been carried out considering three, five and seven membership functions (MFs) of Triangular, Trapezoidal, and Gaussian shapes. Studies reveal that for real-time tuning of the dual input PSS, the NFS network with three MFs of any shape is adequate. The proposed IDIPSS exhibits quite a robust performance to wide variations in loading condition, system parameters and large perturbations.