Dual input power system stabilizer; Neuro-fuzzy controllers; Adaptive network fuzzy inference system; Intelligent controllers; Adaptive power system stabilizer
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 (Pe), reactive power (Qe), and terminal voltage (Vt) characterizing the operating condition are input signals to the network while optimum DIPSS parameters KS1 and T1 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.