A genetic algorithm solution to the governor-turbine dynamic model identification in multi-machine power systems

Stefopoulos, GK; Georgilakis, PS; Hatziargyriou, ND; Meliopoulos, APS

dc.contributor.author	Stefopoulos, GK	en
dc.contributor.author	Georgilakis, PS	en
dc.contributor.author	Hatziargyriou, ND	en
dc.contributor.author	Meliopoulos, APS	en
dc.date.accessioned	2014-03-01T02:43:03Z
dc.date.available	2014-03-01T02:43:03Z
dc.date.issued	2005	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/31202
dc.subject	Dynamic Model	en
dc.subject	Electric Power System	en
dc.subject	Genetic Algorithm	en
dc.subject	Optimization Technique	en
dc.subject	Parameter Estimation	en
dc.subject	Power System	en
dc.subject	Power System Transients	en
dc.subject	Power Variation	en
dc.subject	Real Coded Genetic Algorithm	en
dc.subject.other	Dynamic model identification	en
dc.subject.other	Real coded	en
dc.subject.other	Transient operation	en
dc.subject.other	Computer simulation	en
dc.subject.other	Electric power systems	en
dc.subject.other	Genetic algorithms	en
dc.subject.other	Mathematical models	en
dc.subject.other	Optimization	en
dc.subject.other	Parameter estimation	en
dc.subject.other	Turbines	en
dc.title	A genetic algorithm solution to the governor-turbine dynamic model identification in multi-machine power systems	en
heal.type	conferenceItem	en
heal.identifier.primary	10.1109/CDC.2005.1582336	en
heal.identifier.secondary	http://dx.doi.org/10.1109/CDC.2005.1582336	en
heal.identifier.secondary	1582336	en
heal.publicationDate	2005	en
heal.abstract	Speed governors are key elements in the dynamic performance of electric power systems. Therefore, accurate governor models are of great importance in simulating and investigating the power system transient phenomena. Model parameters of such devices are, however, usually unavailable or inaccurate, especially when old generators are involved. Most methods for speed governor parameter estimation are based on measurements of frequency and active power variations during transient operation. This paper proposes a genetic algorithm based optimization technique for parameter estimation, which makes use of such measurements. The proposed methodology uses a real-coded genetic algorithm. The paper estimates the parameters of all system generators simultaneously, instead of every machine independently, which is fully in line with the interest to treat the electric power system as a whole and study its comprehensive behaviour. Moreover, the methodology is not model-dependent and, therefore, it is readily applicable to a variety of model types and for many different test procedures. The proposed methodology is applied to the electric power system of Crete and the results demonstrate the feasibility and practicality of this approach. © 2005 IEEE.	en
heal.journalName	Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05	en
dc.identifier.doi	10.1109/CDC.2005.1582336	en
dc.identifier.volume	2005	en
dc.identifier.spage	1288	en
dc.identifier.epage	1294	en