Open AccessParameters identification of reduced governor system model for diesel‐engine generator by using hybrid particle swarm optimisation
Author(s) -
Lin ChienHung,
Wu ChiJui,
Yang JunZhe,
Liao ChingJung
Publication year - 2018
Publication title -
iet electric power applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.815
H-Index - 97
eISSN - 1751-8679
pISSN - 1751-8660
DOI - 10.1049/iet-epa.2017.0851
Subject(s) - particle swarm optimization , governor , control theory (sociology) , electric power system , system identification , engineering , diesel generator , power (physics) , stability (learning theory) , hybrid system , usable , computer science , control engineering , automotive engineering , diesel fuel , control (management) , data modeling , algorithm , artificial intelligence , aerospace engineering , physics , software engineering , quantum mechanics , machine learning , world wide web
This study presents an approach to build a reduced‐order model (ROM) for the governor control systems of diesel‐engine generators in an island power system. The hybrid particle swarm optimisation (PSO) is used in the parameter identification of the ROM. The reduced‐order governor system model could be a useful and feasible model in the stability analysis of the island power system by using power system simulator for engineering. The results of the ROM and a sixth‐order model have been compared. It is found that the ROM with the parameter values identified using the hybrid PSO is robust. Moreover, real‐case validation of the ROM shows that it is usable to analyse stability and contingency in the power system.