Title :
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
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;
Conference_Titel :
Electrical Sciences and Technologies in Maghreb (CISTEM), 2014 International Conference on
DOI :
10.1109/CISTEM.2014.7076932
