Fault diagnosis of electrical power transformer based on water content analysis using Bayesian network

Author

Lakehal, Abdelaziz ; Ghemari, Zine ; Chouaki, Karima ; Kherrour, Fatma Zohra

Author_Institution

Dept. of Ind. Eng. & Maintenance, Nat. Higher Sch. of Technol., Algiers, Algeria

fYear

2014

Firstpage

1

Lastpage

5

Abstract

Water content and breakdown voltage of dielectric oil are generally unstable parameters. Exceeding limit permissible threshold of one of parameters implies corrective actions because they are directly related to the oil ability to isolate. In this paper a model based on a Bayesian network (BN) is used to diagnose the causes of transformer failures. The proposed model is used to diagnose the water content in the oil, and to predict the breakdown voltage. A case study of a main transformer (MT) of a power plant is presented to show the effectiveness of our model.

Keywords

belief networks; electric breakdown; fault diagnosis; power transformer insulation; transformer oil; Bayesian network; breakdown voltage; dielectric oil; electrical power transformer; fault diagnosis; limit permissible threshold; main transformer; power plant; transformer failure; water content analysis; water content diagnosis; Bayes methods; Fault diagnosis; Gold; Industrial engineering; Maintenance engineering; Power transformer insulation; Bayesian network; breakdown voltage; electrical power transformer; fault diagnosis; water content;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Sciences and Technologies in Maghreb (CISTEM), 2014 International Conference on

Type

conf

DOI

10.1109/CISTEM.2014.7076932

Filename

7076932