Probabilistic Feedforward Neural Network Based Power System Stabilizer for Excitation Control System of Synchronous Generator

Abstract

– An economical and reliable power system is responsible to generate and deliver electric power in an efficient way by controlling terminal voltage and load frequency within permissible limits. An excitation system plays major role in the stability of power system. The high gain and fast action of an Automatic Voltage Regulator (AVR) produces negative damping oscillation in the power system. To reduce these oscillations, Power System Stabilizer (PSS) is connected in conjunction with excitation system. The PSS has been tuned to cope with the changing load conditions. For this purpose, Probabilistic Feedforward Neural Network (PFNN) based power system stabilizer is proposed. The conventional PSS is designed and simulated in Matlab and the frequency deviation and terminal voltage are stored in order to train the Pobabilistic Neural Network (PNN). The simulation results for terminal voltage and load frequency with conventional PSS and PNN based PSS have been compared. The simulation results of PNN based PSS shows that this type of PSS has great control on the oscillations that are produced by AVR. It has also been observed that this type of PSS can enhance dynamic and transient stability of power system with wide range of operating conditions.