{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/102499"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/102499","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Optimization algorithms for loading military diesel generators","abstract":"The economic load dispatch (ELD) problem challenges the designer to adequately provide for electrical load demand while minimizing operational costs. The military has a unique set of constraints for meeting the ELD problem to provide power to soldiers in forward operating bases. The constraints include the use of military diesel gensets that remain disconnected from each other and are loaded below a user-defined real power threshold (for a reliability safety cushion). In addition, the system must be simple enough to be constructed with minimal training and require no reconfiguration once established. As a result, a simple tool to quickly assign loads to isolated military diesel generators is required. To meet this need, this study compares the use of several optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), cuckoo search (CS), first fit decreasing (FFD) bin packing, and an exhaustive search (ES) method. It is found that at large enough search spaces, the optimization algorithms can discover reasonably optimal solutions while substantially decreasing search time. For this application, FFD has more optimal average solutions as well as faster run time compared to the other algorithms.","abstract_html":"The economic load dispatch (ELD) problem challenges the designer to adequately provide for electrical load demand while minimizing operational costs. The military has a unique set of constraints for meeting the ELD problem to provide power to soldiers in forward operating bases. The constraints include the use of military diesel gensets that remain disconnected from each other and are loaded below a user-defined real power threshold (for a reliability safety cushion). In addition, the system must be simple enough to be constructed with minimal training and require no reconfiguration once established. As a result, a simple tool to quickly assign loads to isolated military diesel generators is required. To meet this need, this study compares the use of several optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), cuckoo search (CS), first fit decreasing (FFD) bin packing, and an exhaustive search (ES) method. It is found that at large enough search spaces, the optimization algorithms can discover reasonably optimal solutions while substantially decreasing search time. For this application, FFD has more optimal average solutions as well as faster run time compared to the other algorithms.","abstract_has_math":false,"creators":["Peterson, Nathan"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Sauer, Peter W.","Johnson, Melanie"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-02-06T19:36:40Z","date_published":"2019-02-06T19:36:40Z","updated_at":"2026-07-22T22:24:42Z","subjects":["Economic Load Dispatch","ELD","Particle Swarm Optimization","PSO","Cuckoo Search","CS","CSO","Bat algorithm","BA","First Fit Decreasing","FFD","generator","optimization algorithm","swarm","metaheuristic"],"languages":["en"],"rights":["Copyright 2018 Nathan Peterson"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/102499","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Sauer, Peter W.","Johnson, Melanie"]},{"key":"dc:creator","label":"Author","values":["Peterson, Nathan"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-02-06T19:36:40Z","2018-12-07","2018-12"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Economic Load Dispatch","ELD","Particle Swarm Optimization","PSO","Cuckoo Search","CS","CSO","Bat algorithm","BA","First Fit Decreasing","FFD","generator","optimization algorithm","swarm","metaheuristic"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018 Nathan Peterson"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/102499"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["The economic load dispatch (ELD) problem challenges the designer to adequately provide for electrical load demand while minimizing operational costs. The military has a unique set of constraints for meeting the ELD problem to provide power to soldiers in forward operating bases. The constraints include the use of military diesel gensets that remain disconnected from each other and are loaded below a user-defined real power threshold (for a reliability safety cushion). In addition, the system must be simple enough to be constructed with minimal training and require no reconfiguration once established. As a result, a simple tool to quickly assign loads to isolated military diesel generators is required. To meet this need, this study compares the use of several optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), cuckoo search (CS), first fit decreasing (FFD) bin packing, and an exhaustive search (ES) method. It is found that at large enough search spaces, the optimization algorithms can discover reasonably optimal solutions while substantially decreasing search time. For this application, FFD has more optimal average solutions as well as faster run time compared to the other algorithms.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Nathan Peterson, accepted the attached license on 2018-12-07 at 14:07.","The student, Nathan Peterson, submitted this Thesis for approval on 2018-12-07 at 14:38.","This Thesis was approved for publication on 2018-12-07 at 15:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13254 on 2019-02-05 at 11:15:50","Made available in DSpace on 2019-02-06T19:36:40Z (GMT). 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The constraints include the use of military diesel gensets that remain disconnected from each other and are loaded below a user-defined real power threshold (for a reliability safety cushion). In addition, the system must be simple enough to be constructed with minimal training and require no reconfiguration once established. As a result, a simple tool to quickly assign loads to isolated military diesel generators is required. To meet this need, this study compares the use of several optimization algorithms including particle swarm optimization (PSO), bat algorithm (BA), cuckoo search (CS), first fit decreasing (FFD) bin packing, and an exhaustive search (ES) method. It is found that at large enough search spaces, the optimization algorithms can discover reasonably optimal solutions while substantially decreasing search time. For this application, FFD has more optimal average solutions as well as faster run time compared to the other algorithms.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2019-02-05 without embargo terms","The student, Nathan Peterson, accepted the attached license on 2018-12-07 at 14:07.","The student, Nathan Peterson, submitted this Thesis for approval on 2018-12-07 at 14:38.","This Thesis was approved for publication on 2018-12-07 at 15:04.","DSpace SAF Submission Ingestion Package generated from Vireo submission #13254 on 2019-02-05 at 11:15:50","Made available in DSpace on 2019-02-06T19:36:40Z (GMT). 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