Reinforcement learning (RL) to optimal reconfiguration of radial distribution system (RDS)

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dc.contributor.author Vlachogiannis, JG en
dc.contributor.author Hatziargyriou, N en
dc.date.accessioned 2014-03-01T02:42:57Z
dc.date.available 2014-03-01T02:42:57Z
dc.date.issued 2004 en
dc.identifier.issn 0302-9743 en
dc.identifier.uri http://hdl.handle.net/123456789/31147
dc.subject Closed Loop Control en
dc.subject Decision Problem en
dc.subject Distributed System en
dc.subject Evolutionary Programming en
dc.subject Learning Algorithm en
dc.subject Reinforcement Learning en
dc.subject Satisfiability en
dc.subject.classification Computer Science, Theory & Methods en
dc.subject.other Busbars en
dc.subject.other Closed loop control systems en
dc.subject.other Decision making en
dc.subject.other Electric potential en
dc.subject.other Energy dissipation en
dc.subject.other Learning algorithms en
dc.subject.other Mapping en
dc.subject.other Optimization en
dc.subject.other Radial basis function networks en
dc.subject.other Reinforcement en
dc.subject.other Evolutionary programming (EP) en
dc.subject.other Power loss en
dc.subject.other Radial distribution systems (RDS) en
dc.subject.other Reinforcement learning (RL) en
dc.subject.other Learning systems en
dc.title Reinforcement learning (RL) to optimal reconfiguration of radial distribution system (RDS) en
heal.type conferenceItem en
heal.identifier.primary 10.1007/978-3-540-24674-9_46 en
heal.identifier.secondary http://dx.doi.org/10.1007/978-3-540-24674-9_46 en
heal.language English en
heal.publicationDate 2004 en
heal.abstract This paper presents a Reinforcement Learning (RL) method for optimal reconfiguration of radial distribution system (RDS). Optimal reconfiguration involves selection of the best set of branches to be opened, one from each loop, such that the resulting RDS has the desired performance. Among the several performance criteria considered for optimal network reconfiguration, an important one is real power losses minimization, while satisfying voltage limits. The RL method formulates the reconfiguration of RDS as a multistage decision problem. More specifically, the model-free learning algorithm (Q-learning) learns by experience how to adjust a closed-loop control rule mapping operating states to control actions by means of reward values. Rewards are chosen to express how well control actions cause minimization of power losses. The Q-learning algorithm is applied to the reconfiguration of 33-bus RDS busbar system. The results are compared with those given by other evolutionary programming methods. en
heal.publisher SPRINGER-VERLAG BERLIN en
heal.journalName Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) en
dc.identifier.doi 10.1007/978-3-540-24674-9_46 en
dc.identifier.isi ISI:000221610800046 en
dc.identifier.volume 3025 en
dc.identifier.spage 439 en
dc.identifier.epage 446 en

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