Determination of transformer health condition using artificial neural networks

Author

Abu-Elanien, Ahmed E B ; Salama, M.M.A. ; Ibrahim, Malak

Author_Institution

Dept. of Electr. & Comput. Eng., Univ. of Waterloo, Waterloo, ON, Canada

fYear

2011

fDate

15-18 June 2011

Firstpage

1

Lastpage

5

Abstract

This paper presents a method to estimate a transformer health condition based on diagnostic tests. A feed forward artificial neural network (FFANN) is used to find the health index of the transformer. The health index is used to find the health condition of the transformer. The training of the FFANN is done using real measurements of 59 working transformers. The testing of the trained neural network performance is done using real data for 29 working transformers. The performance evaluation of the trained FFANN shows that the trained neural network is reliable in finding the health condition of any working transformer.

Keywords

condition monitoring; feedforward neural nets; power engineering computing; power system reliability; power transformers; feedforward artificial neural network; transformer health condition; transformer health index; Artificial neural networks; Indexes; Oil insulation; Power transformer insulation; Testing; Asset management; condition monitoring; dissolved gas analysis; furans analysis; health condition; health index; transformer;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA), 2011 International Symposium on

Conference_Location

Istanbul

Print_ISBN

978-1-61284-919-5

Type

conf

DOI

10.1109/INISTA.2011.5946173

Filename

5946173