A comparative study of neural network efficiency in power transformers diagnosis using dissolved gas analysis

Author

Guardado, J.L. ; Naredo, J.L. ; Moreno, P. ; Fuerte, C.R.

Author_Institution

ITM, Merelia Mich, Mexico

Volume

16

Issue

4

fYear

2001

fDate

10/1/2001 12:00:00 AM

Firstpage

643

Lastpage

647

Abstract

This paper presents a comparative study of neural network (NN) efficiency for the detection of incipient faults in power transformers. The NN was trained according to five diagnosis criteria commonly used for dissolved gas analysis (DGA) in transformer insulating oil. These criteria are Doernenburg, modified Rogers, Rogers, IEC and CSUS. Once trained, the neural network was tested by using a new set of DGA results. Finally, NN diagnosis results were compared with those obtained by inspection and an analysis. The study shows that NN rate of successful diagnosis is dependant on the criterion under consideration, with values in the range of 87-100%

Keywords

automatic test software; chemical analysis; inspection; insulation testing; learning (artificial intelligence); neural nets; power transformer insulation; power transformer testing; transformer oil; diagnosis criteria; dissolved gas analysis; incipient fault detection; neural network efficiency; power transformers diagnosis; training; transformer insulating oil; Dissolved gas analysis; Fault detection; Gas insulation; IEC; Neural networks; Oil insulation; Petroleum; Power transformer insulation; Power transformers; Testing;

fLanguage

English

Journal_Title

Power Delivery, IEEE Transactions on

Publisher

ieee

ISSN

0885-8977

Type

jour

DOI

10.1109/61.956751

Filename

956751