A neural fuzzy approach to modeling the thermal behavior of power transformers

1495 648 1105 5 17620 document 1105 Nguyen.pdf application/pdf 1387075 2011-09-09 10:21:50 https://vuir.vu.edu.au/1495/1/Nguyen.pdf 1495 1 application/pdf text en public

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http://eprints.org/relation/isVersionOf https://vuir.vu.edu.au/id/dark_document/73476 http://eprints.org/relation/isVolatileVersionOf https://vuir.vu.edu.au/id/dark_document/73476 http://eprints.org/relation/issmallThumbnailVersionOf https://vuir.vu.edu.au/id/dark_document/73476 archive 116 disk0/00/00/14/95 2008-10-27 03:05:39 2021-08-27 07:41:33 2008-10-27 03:05:39 thesis show Nguyen Huy Huynh A neural fuzzy approach to modeling the thermal behavior of power transformers unpub 290000 tafeengineering power transformers, ANSI/IEEE standard models, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN), Elman Recurrent Neural Network (ERNN) 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. 2007 submitted 73476 2 2416158 dark_document 73476 Nguyen, Huy MEng.pdf application/pdf cdc146f18b12050417567e8bb283ffbb MD5 144597 2020-11-04 01:10:55 1495 3 3 application/pdf text VURR permission form en staffonly

Nguyen, Huy MEng.pdf

other public 123 Victoria University Faculty of Health, Engineering & Science rmaster Includes bibliographic references: pp. 106-110 FALSE