A particle swarm optimization method for power system dynamic security control

Voumvoulakis, EM; Hatziargyriou, ND

dc.contributor.author	Voumvoulakis, EM	en
dc.contributor.author	Hatziargyriou, ND	en
dc.date.accessioned	2014-03-01T01:32:29Z
dc.date.available	2014-03-01T01:32:29Z
dc.date.issued	2010	en
dc.identifier.issn	0885-8950	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/20164
dc.subject	Artificial intelligence	en
dc.subject	Corrective control	en
dc.subject	Dynamic security	en
dc.subject	Load shedding	en
dc.subject	Particle swarm optimization	en
dc.subject	Radial basis function neural network	en
dc.subject.classification	Engineering, Electrical & Electronic	en
dc.subject.other	Added values	en
dc.subject.other	Automatic-learning	en
dc.subject.other	Corrective control	en
dc.subject.other	Corrective control actions	en
dc.subject.other	Dynamic security	en
dc.subject.other	Load-shedding	en
dc.subject.other	Machine learning methods	en
dc.subject.other	Objective functions	en
dc.subject.other	Optimal controls	en
dc.subject.other	Particle swarm optimization method	en
dc.subject.other	Power system dynamics	en
dc.subject.other	Power systems	en
dc.subject.other	Radial basis function neural networks	en
dc.subject.other	Realistic model	en
dc.subject.other	Test systems	en
dc.subject.other	Attitude control	en
dc.subject.other	Electric power systems	en
dc.subject.other	Learning systems	en
dc.subject.other	Neural networks	en
dc.subject.other	Optical communication	en
dc.subject.other	Radial basis function networks	en
dc.subject.other	Particle swarm optimization (PSO)	en
dc.title	A particle swarm optimization method for power system dynamic security control	en
heal.type	journalArticle	en
heal.identifier.primary	10.1109/TPWRS.2009.2031224	en
heal.identifier.secondary	http://dx.doi.org/10.1109/TPWRS.2009.2031224	en
heal.identifier.secondary	5291695	en
heal.language	English	en
heal.publicationDate	2010	en
heal.abstract	This paper proposes an automatic learning framework for the dynamic security control of a power system. The proposed method employs a radial basis function neural network (RBFNN), which serves to assess the dynamic security status of the power system and to estimate the effect of a corrective control action applied in the event of a disturbance. Particle swarm optimization is applied to find the optimal control action, where the objective function to be optimized is provided by the RBFNN. The method is applied on a realistic model of the Hellenic Power System and on the IEEE 50-generator test system, and its added value is shown by comparing results with the ones obtained from the application of other machine learning methods. © 2010 IEEE.	en
heal.publisher	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC	en
heal.journalName	IEEE Transactions on Power Systems	en
dc.identifier.doi	10.1109/TPWRS.2009.2031224	en
dc.identifier.isi	ISI:000285051800046	en
dc.identifier.volume	25	en
dc.identifier.issue	2	en
dc.identifier.spage	1032	en
dc.identifier.epage	1041	en