Title
Power System Stabilizer Design with meta-heuristic methods in a Peruvian case study
Date Issued
01 January 2022
Access level
metadata only access
Resource Type
conference paper
Publisher(s)
Institute of Electrical and Electronics Engineers Inc.
Abstract
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.
Language
English
OCDE Knowledge area
Ingeniería eléctrica, Ingeniería electrónica
Publication version
Version of Record
Scopus EID
2-s2.0-85138808119
ISBN of the container
978-166548636-1
Conference
Conference Proceedings: Proceedings of the 2022 IEEE 29th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2022
Sources of information: Directorio de Producción Científica Universidad Privada Peruano Alemana Scopus