{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/99123"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/99123","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Modeling and maximizing power in wind turbine arrays","abstract":"This work considers a specific application domain, that of wind turbine arrays, and explores algorithms for determining individual axial induction factors that optimize overall energy extraction. Large wind turbine arrays, or wind farms, can be viewed as large coupled networks, for which the application of traditional optimization techniques are impractical. A brief discussion on wind farm models and traditional optimization approaches leads to a dynamic programming approach to maximize power extraction under the condition of uniform wind. This differs from prior work in which only a dynamic programming approach for a near-field approximate solution has been analyzed. Following that, a heuristic method to find solutions to both the near-field and the far-field problems is presented. Simulation results are discussed, which demonstrate the algorithm provides improved performance compared to prior work on near-field approaches.","abstract_html":"This work considers a specific application domain, that of wind turbine arrays, and explores algorithms for determining individual axial induction factors that optimize overall energy extraction. Large wind turbine arrays, or wind farms, can be viewed as large coupled networks, for which the application of traditional optimization techniques are impractical. A brief discussion on wind farm models and traditional optimization approaches leads to a dynamic programming approach to maximize power extraction under the condition of uniform wind. This differs from prior work in which only a dynamic programming approach for a near-field approximate solution has been analyzed. Following that, a heuristic method to find solutions to both the near-field and the far-field problems is presented. Simulation results are discussed, which demonstrate the algorithm provides improved performance compared to prior work on near-field approaches.","abstract_has_math":false,"creators":["Buccafusca, Lucas D"],"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":["Beck, Carolyn L."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-03-02T19:59:46Z","date_published":"2018-03-02T19:59:46Z","updated_at":"2026-07-22T22:24:37Z","subjects":["Optimization","Wind energy","Dynamic programming"],"languages":["en"],"rights":["Copyright 2017 Lucas Buccafusca"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/99123","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Beck, Carolyn L."]},{"key":"dc:creator","label":"Author","values":["Buccafusca, Lucas D"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-03-02T19:59:46Z","2020-03-03T10:15:29Z","2017-07-19","2017-08"]},{"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":["Optimization","Wind energy","Dynamic programming"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2017 Lucas Buccafusca"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/99123"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["This work considers a specific application domain, that of wind turbine arrays, and explores algorithms for determining individual axial induction factors that optimize overall energy extraction. Large wind turbine arrays, or wind farms, can be viewed as large coupled networks, for which the application of traditional optimization techniques are impractical. A brief discussion on wind farm models and traditional optimization approaches leads to a dynamic programming approach to maximize power extraction under the condition of uniform wind. This differs from prior work in which only a dynamic programming approach for a near-field approximate solution has been analyzed. Following that, a heuristic method to find solutions to both the near-field and the far-field problems is presented. 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Large wind turbine arrays, or wind farms, can be viewed as large coupled networks, for which the application of traditional optimization techniques are impractical. A brief discussion on wind farm models and traditional optimization approaches leads to a dynamic programming approach to maximize power extraction under the condition of uniform wind. This differs from prior work in which only a dynamic programming approach for a near-field approximate solution has been analyzed. Following that, a heuristic method to find solutions to both the near-field and the far-field problems is presented. 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