An optimization of robust SMES with specified structure H∞ controller for power system stabilization considering superconducting magnetic coil size

dc.contributor.authorNgamroo, Issarachai
dc.date.accessioned2026-08-06T10:01:43Z
dc.date.available2026-08-06T10:01:43Z
dc.date.issued2011-01-01
dc.description.abstractEven the superconducting magnetic energy storage (SMES) is the smart stabilizing device in electric power systems, the installation cost of SMES is very high. Especially, the superconducting magnetic coil size which is the critical part of SMES, must be well designed. On the contrary, various system operating conditions result in system uncertainties. The power controller of SMES designed without taking such uncertainties into account, may fail to stabilize the system. By considering both coil size and system uncertainties, this paper copes with the optimization of robust SMES controller. No need of exact mathematic equations, the normalized coprime factorization is applied to model system uncertainties. Based on the normalized integral square error index of inter-area rotor angle difference and specified structured H <inf>∞</inf> loop shaping optimization, the robust SMES controller with the smallest coil size, can be achieved by the genetic algorithm. The robustness of the proposed SMES with the smallest coil size can be confirmed by simulation study. © 2010 Elsevier Ltd. All rights reserved.
dc.identifier.citationEnergy Conversion and Management, 52(1), 648-651, 2011
dc.identifier.doi10.1016/j.enconman.2010.07.042
dc.identifier.issn01968904
dc.identifier.other2-s2.0-80054730055
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/3473
dc.sourceEnergy Conversion and Management
dc.subjectGenetic algorithm
dc.subjectPower system stabilization
dc.subjectSuperconducting magnetic energy storage
dc.subjectSystem uncertainties
dc.titleAn optimization of robust SMES with specified structure H∞ controller for power system stabilization considering superconducting magnetic coil size
dc.typeConference Paper

Files

Collections