Fault prognosis in power transformers using adaptive-network-based fuzzy inference system

Article type: Research Article

Authors: Zarei, Jafar | Shasadeghi, Mokhtar | Ramezani, Abdolrahman

Affiliations: Faculty of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran

Note: [] Corresponding author. Jafar Zarei, Faculty of Electrical and Electronic Engineering, Shiraz University of Technology, Shiraz, Iran. E-mail: zarei@sutech.ac.ir

Abstract: In this paper a new method for estimating dissolved gases of power transformers in the terms of operation time of power transformers using adaptive-network-based fuzzy inference system is presented. Then, transformer condition is evaluated via dissolved gas analysis (DGA) according to IEC 60599 standard. Prominent features of the proposed method are high accuracy, reliability, and high speed convergence in real-world application in spite of the limited numbers of data. The proposed methodology has been evaluated for several real cases while the transformers are operating, and the efficiency of the proposed method is verified.

Keywords: Adaptive-network-based fuzzy inference system (ANFIS), power transformers, type-2 fuzzy logic systems, diagnostic reasoning

DOI: 10.3233/IFS-130929

Journal: Journal of Intelligent & Fuzzy Systems, vol. 26, no. 5, pp. 2577-2590, 2014