{"id":{"repo_id":"vu-aus","oai_identifier":"oai:eprints.vu.edu.au:1495"},"canonical_url":"https://search.dev.ndltd.org/etd/vu-aus/oai:eprints.vu.edu.au:1495","repository":{"repo_id":"vu-aus","name":"Victoria University (Australia)","base_url":"https://vuir.vu.edu.au/cgi/oai2"},"display":{"title":"A neural fuzzy approach to modeling the thermal behavior of power transformers","abstract":"This thesis presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard models, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top and bottom-oil temperatures for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. The models were derived from real data of temperature measurements obtained from two industrial power installations. A comparison of the proposed techniques is presented for predicting top and bottom-oil temperatures based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparisons of the results obtained indicate that the hybrid neuro-fuzzy network is the best candidate for the analysis and prediction of the power transformer top and bottom-oil temperatures. The ANFIS demonstrated the best comparative performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peak error.","abstract_html":"This thesis presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard models, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top and bottom-oil temperatures for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. The models were derived from real data of temperature measurements obtained from two industrial power installations. A comparison of the proposed techniques is presented for predicting top and bottom-oil temperatures based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparisons of the results obtained indicate that the hybrid neuro-fuzzy network is the best candidate for the analysis and prediction of the power transformer top and bottom-oil temperatures. The ANFIS demonstrated the best comparative performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peak error.","abstract_has_math":false,"creators":["Nguyen, Huy Huynh"],"institution":"Victoria University","degree_name":null,"degree_level":"rmaster","degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2007,"date_issued":"2007","date_published":"2007","updated_at":"2026-07-24T06:33:03Z","subjects":["290000 Engineering and Technology","School of Engineering and Science"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Nguyen, Huy Huynh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2007"]},{"key":"dc:date.issued","label":"Date","values":["2007"]},{"key":"dc:publisher.department","label":"Dc Publisher Department","values":["Faculty of Health, Engineering & Science"]},{"key":"dc:publisher.institution","label":"Dc Publisher Institution","values":["Victoria University"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://vuir.vu.edu.au/1495/"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]},{"key":"dc:type.qualificationlevel","label":"Dc Type Qualificationlevel","values":["rmaster"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["290000 Engineering and Technology","School of Engineering and Science"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://vuir.vu.edu.au/1495/1/Nguyen.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["This thesis presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard models, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top and bottom-oil temperatures for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. The models were derived from real data of temperature measurements obtained from two industrial power installations. A comparison of the proposed techniques is presented for predicting top and bottom-oil temperatures based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparisons of the results obtained indicate that the hybrid neuro-fuzzy network is the best candidate for the analysis and prediction of the power transformer top and bottom-oil temperatures. The ANFIS demonstrated the best comparative performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peak error."]},{"key":"dc:format","label":"Dc Format","values":["text"]},{"key":"dc:title","label":"Title","values":["A neural fuzzy approach to modeling the thermal behavior of power transformers"]}]}],"canonical_facts":{"dc:creator":["Nguyen, Huy Huynh"],"dc:date":["2007"],"dc:date.issued":["2007"],"dc:description.abstract":["This thesis presents an investigation and a comparative study of four different approaches namely ANSI/IEEE standard models, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN) and Elman Recurrent Neural Network (ERNN) to modeling and prediction of the top and bottom-oil temperatures for the 8 MVA Oil Air (OA)-cooled and 27 MVA Forced Air (FA)-cooled class of power transformers. The models were derived from real data of temperature measurements obtained from two industrial power installations. A comparison of the proposed techniques is presented for predicting top and bottom-oil temperatures based on the historical data measured over a 35 day period for the first transformer and 4.5 days for the second transformer with either a half or a quarter hour sampling time. Comparisons of the results obtained indicate that the hybrid neuro-fuzzy network is the best candidate for the analysis and prediction of the power transformer top and bottom-oil temperatures. The ANFIS demonstrated the best comparative performance in temperature prediction in terms of Root Mean Square Error (RMSE) and peak error."],"dc:format":["text"],"dc:identifier.uri":["https://vuir.vu.edu.au/1495/1/Nguyen.pdf"],"dc:language":["en"],"dc:publisher.department":["Faculty of Health, Engineering & Science"],"dc:publisher.institution":["Victoria University"],"dc:relation.isreferencedby":["https://vuir.vu.edu.au/1495/"],"dc:subject":["290000 Engineering and Technology","School of Engineering and Science"],"dc:title":["A neural fuzzy approach to modeling the thermal behavior of power transformers"],"dc:type":["Thesis"],"dc:type.qualificationlevel":["rmaster"]},"updated_at":"2026-07-24T06:33:03Z"}