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

Nguyen, Huy Huynh (2007) A neural fuzzy approach to modeling the thermal behavior of power transformers. Research Master thesis, Victoria University.

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.

Item type Thesis (Research Master thesis)
URI https://vuir.vu.edu.au/id/eprint/1495
Subjects Historical > RFCD Classification > 290000 Engineering and Technology
Historical > Faculty/School/Research Centre/Department > School of Engineering and Science
Keywords power transformers, ANSI/IEEE standard models, Adaptive Neuro-Fuzzy Inference System (ANFIS), Multilayer Feedforward Neural Network (MFNN), Elman Recurrent Neural Network (ERNN)
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