Please use this identifier to cite or link to this item: https://doi.org/10.1109/PMAPS.2010.5529004
DC FieldValue
dc.titleGenerator maintenance scheduling with hybrid evolutionary algorithm
dc.contributor.authorSrinivasan, D.
dc.contributor.authorAik, K.C.
dc.contributor.authorMalik, I.M.
dc.date.accessioned2014-06-19T03:11:58Z
dc.date.available2014-06-19T03:11:58Z
dc.date.issued2010
dc.identifier.citationSrinivasan, D.,Aik, K.C.,Malik, I.M. (2010). Generator maintenance scheduling with hybrid evolutionary algorithm. 2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2010 : 632-637. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/PMAPS.2010.5529004" target="_blank">https://doi.org/10.1109/PMAPS.2010.5529004</a>
dc.identifier.isbn9781424457236
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/70422
dc.description.abstractThis paper proposes a hybrid evolutionary algorithm to solve the maintenance-scheduling problem for thermal generating units. The proposed approach uses a hybrid Fuzzy-Genetic Algorithm that implements Fuzzy Knowledge Based System to emulate the power plant personnel's experience, and uncertainties in the constraints, while a Genetic Algorithm optimizes the total generating cost and the maintenance cost as the objective functions. Two other effective and practical methods based on Evolution Strategy and Particle Swarm Optimization were also applied for the same problem. Simulations were carried out on a practical thermal power plant consisting of 19 generating units, over a six-month planning horizon. ©2010 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/PMAPS.2010.5529004
dc.sourceScopus
dc.subjectArtificial intelligence
dc.subjectEvolution strategy
dc.subjectFuzzy systems
dc.subjectGenetic algorithm
dc.subjectHybrid intelligent systems
dc.subjectMaintenance schedule
dc.subjectParticle swarm optimization
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/PMAPS.2010.5529004
dc.description.sourcetitle2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power Systems, PMAPS 2010
dc.description.page632-637
dc.identifier.isiutNOT_IN_WOS
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