Nonlinear Autoregressive Neural Network Models for Prediction of Transformer Oil-Dissolved Gas Concentrations

Fabio Henrique Pereira, Francisco Elânio Bezerra, Shigueru Junior, Josemir Santos, Ivan Chabu, Gilberto Francisco Martha de Souza, Fábio Micerino and Silvio Ikuyo Nabeta
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Fabio Henrique Pereira: Informatics and Knowledge Management Graduate Program, Universidade Nove de Julho, São Paulo 01504-000, Brazil
Francisco Elânio Bezerra: Industrial Engineering Graduate Program, Universidade Nove de Julho, São Paulo 01504-000, Brazil
Shigueru Junior: Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil
Josemir Santos: Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil
Ivan Chabu: Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil
Gilberto Francisco Martha de Souza: Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil
Fábio Micerino: EDP Energias do Brasil, São Paulo 4547006, Brazil
Silvio Ikuyo Nabeta: Polytechnic School, Universidade de São Paulo, São Paulo 05508-010, Brazil

Energies, 2018, vol. 11, issue 7, 1-12

Abstract: Transformers are one of the most important part in a power system and, especially in key-facilities, they should be closely and continuously monitored. In this context, methods based on the dissolved gas ratios allow to associate values of gas concentrations with the occurrence of some faults, such as partial discharges and thermal faults. So, an accurate prediction of oil-dissolved gas concentrations is a valuable tool to monitor the transformer condition and to develop a fault diagnosis system. This study proposes a nonlinear autoregressive neural network model coupled with the discrete wavelet transform for predicting transformer oil-dissolved gas concentrations. The data fitting and accurate prediction ability of the proposed model is evaluated in a real world example, showing better results in relation to current prediction models and common time series techniques.

Keywords: oil-dissolved gas; nonlinear autoregressive neural network; fault diagnose system (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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