This paper presents an approach to the design of self-tuning adaptive power system stabilizer which is based on artificial neural network. Result shows that ANN based power system stabilizer can provide good damping for both local and inter area modes of oscillations. An ANN is used for self-tuning the different parameters of PSS like stabilizing gain Kstab and time constant (T1) for Lead PSS i...

Damping of power system oscillations with the help of proposed optimal Proportional Integral Derivative Power System Stabilizer (PID-PSS) and Static Var Compensator (SVC)-based controllers are thoroughly investigated in this paper. This study presents robust tuning of PID-PSS and SVC-based controllers using Genetic Algorithms (GA) in multi machine power systems by considering detailed model of ...

A new algorithm for the design of decentralized output feedback stabilizers for large-scale electric power systems is presented in this paper. In the proposed approach, the generators which are most effective for stabilizer applications are first identified by using participation factors. Then an efficient algorithm based on decentralized pole assignment is proposed for the determination of the...

This paper presents a new two-layer recurrent neural network (RNN) for a power system stabilizer (PSS) design called the recurrent neural network power system stabilizer (RNNPSS). The RNNPSS consists of a recurrent neural network identifier (RNNI) that tracks and identifies the power generator and a recurrent neural network controller (RNNC) that supplies an adaptive signal to the governor and ...