Sensing Cloud Optimization applied to a non-convex constrained economical dispatch

dc.contributor.author Fonte,PM en
dc.contributor.author Cláudio Monteiro en
dc.contributor.author Fernando Maciel Barbosa en
dc.date.accessioned 2017-12-14T16:47:52Z
dc.date.available 2017-12-14T16:47:52Z
dc.date.issued 2013 en
dc.description.abstract In this paper it is intended to solve an Economical Dispatch (ED) problem with a new tool, named Sensing Cloud Optimization (SCO). It is a technique based on clouds of particles which allow a dynamic change in search space. It has appropriate heuristic characteristic to solve not convex, not differentiable and highly constrained optimisation problems. It is provided with a statistical analysis which determines the cloud's dimension with dynamic adjustments in search space in order to accelerate the convergence and to avoid to get trapped in local minima. Two case studies are presented in which SCO demonstrated good performances reaching lower cost values where compared with other techniques. en
dc.identifier.uri http://repositorio.inesctec.pt/handle/123456789/4112
dc.identifier.uri http://dx.doi.org/10.1109/iecon.2013.6699466 en
dc.language eng en
dc.relation 4911 en
dc.rights info:eu-repo/semantics/openAccess en
dc.title Sensing Cloud Optimization applied to a non-convex constrained economical dispatch en
dc.type conferenceObject en
dc.type Publication en
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