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Ant colony system-based algorithm for constrained load flow problem

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dc.contributor.author Vlachogiannis, JG en
dc.contributor.author Hatziargyriou, ND en
dc.contributor.author Lee, KY en
dc.date.accessioned 2014-03-01T01:21:51Z
dc.date.available 2014-03-01T01:21:51Z
dc.date.issued 2005 en
dc.identifier.issn 0885-8950 en
dc.identifier.uri https://dspace.lib.ntua.gr/xmlui/handle/123456789/16398
dc.subject Ant colony system (ACS) en
dc.subject Combinatorial optimization en
dc.subject Constrained load flow (CLF) en
dc.subject Reinforcement learning (RL) en
dc.subject.classification Engineering, Electrical & Electronic en
dc.subject.other Algorithms en
dc.subject.other Electric losses en
dc.subject.other Heuristic methods en
dc.subject.other Optimization en
dc.subject.other Power control en
dc.subject.other Probability en
dc.subject.other Reactive power en
dc.subject.other Ant colony system (ACS) en
dc.subject.other Combinatorial optimization en
dc.subject.other Constrained load flow (CLF) en
dc.subject.other Reinforcement learning (RL) en
dc.subject.other Electric load flow en
dc.title Ant colony system-based algorithm for constrained load flow problem en
heal.type journalArticle en
heal.identifier.primary 10.1109/TPWRS.2005.851969 en
heal.identifier.secondary http://dx.doi.org/10.1109/TPWRS.2005.851969 en
heal.language English en
heal.publicationDate 2005 en
heal.abstract This paper presents the ant colony system (ACS) method for network-constrained optimization problems. The developed ACS algorithm formulates the constrained load flow (CLF) problem as a combinatorial optimization problem. It is a distributed algorithm composed of a set of cooperating artificial agents, called ants, that cooperate among them to find an optimum solution of the CLF problem. A pheromone matrix that plays the role of global memory provides the cooperation between ants. The study consists of mapping the solution space, expressed by an objective function of the CLF on the space of control variables [ant system (AS)-graph], that ants walk. The ACS algorithm is applied to the IEEE 14-bus system and the IEEE 136-bus system. The results are compared with those given by the probabilistic CLF and the reinforcement learning (RL) methods, demonstrating the superiority and flexibility of the ACS algorithm. Moreover, the ACS algorithm is applied to the reactive power control problem for the IEEE 14-bus system in order to minimize real power losses subject to operating constraints over the whole planning period. © 2005 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.2005.851969 en
dc.identifier.isi ISI:000231001900006 en
dc.identifier.volume 20 en
dc.identifier.issue 3 en
dc.identifier.spage 1241 en
dc.identifier.epage 1249 en


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