Power System Stabilizer Design with meta-heuristic methods in a Peruvian case study

Velasquez R.M.A.; Ezcurra T.T.P.; Aldon L.F.V. · Proceedings of the 2022 IEEE 29th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2022 (2022) · DOI: 10.1109/intercon55795.2022.9870161

Resumen

Evolutionary Particle Swarm Optimization (EPSO), Particle Swarm Optimization (PSO) and Taboo are Heuristic methods based algorithm motivated by Neural Networks. it has been widely used in nonlinear system. This paper is concerned with solving the parameters identification problem for nonlinear dynamic systems through a control with heuristic methods compared with some traditional techniques in the Power System Stabilizer (PSS). Actually, the IEEE model has a low damping ratio performance, with poorly damped electromechanical modes. Our findings have demonstrated a higher the stability performance than traditional methods; the damping ratio is damped for the electromechanical modes for low frequency oscillations when large power systems are interconnected in a Peruvian power plant, the comparison considered three algorithms with Taboo, PSO, EPSO, for the dynamic control in the PSS.