Small signal stability enhancement of a multi‐machine power system using robust and adaptive fuzzy neural network‐based power system stabilizer

Abstract

AbstractThis paper presents a design procedure for a robust and adaptive fuzzy neural network‐based power system stabilizer (RAFNNPSS) and investigates the robustness and adaptive feature of the RAFNNPSS for a single machine connected to an infinite bus system and multi‐machine power systems in order to enhance the dynamic stability (small signal stability of the system). The parameters of RAFNNPSS are tuned by adaptive neural network (NN). This RAFNNPSS uses adaptive network‐based fuzzy inference system (ANFIS) network, which provides a natural framework of multi‐layered feed forward adaptive network using fuzzy logic inference system. In this approach, the hybrid‐learning algorithm tunes the fuzzy rules and the membership functions of the RAFNNPSS. Speed deviation of synchronous generator and its derivative are chosen as the input signals to the RAFNNPSS. The dynamic performance of single‐machine infinite bus (SMIB) system, a two‐area, five‐machine, eight‐bus power system and a large power system (10‐machine, 39‐bus New England system) with the proposed RAFNNPSS under different operating conditions and change in system parameters have been investigated. The simulation results obtained from the conventional PSS (CPSS) and Fuzzy logic‐based PSS (FPSS) are compared with the proposed RAFNNPSS. The simulation results demonstrate that the proposed RAFNNPSS performs well in damping and quicker response when compared with the other two PSSs. Copyright © 2008 John Wiley & Sons, Ltd.