Novel line search based parameter optimization of multi-machnie power system stabilizer enhanced by teaching learning based optimization

Abstract

In this paper a new effectual optimization approach is proposed which optimizes the power system stabilizers (PSSs) parameters in a multi-machine power system. The PSS parameters are established for four PSSs which are linked to four synchronous generators. There is an increased demand for development of such algorithm, so that made researchers look for algorithms not only being metaheuristic, but also inherit desired properties of so-called deterministic approaches. To accomplish this objective, a novel method called smart line search (SLS) has been proposed in this paper and has been combined with Teaching Learning based optimization (TLBO). Being so new-found, SLS tries to take benefits of gradient methods using weighted stochastic selection approach which not only leads to unraveling of new local optimums, but also with a performance rather more speedy than conventional algorithms so far used. The performance of the proposed approach is also compared with other method. By observing the simulation results in two-area four-machine power system and compare with each of old algorithm, have been seen that new method is positively effective on damping time and also power frequency and oscillation when the fault occurs.