Estimating the DP Value of the Paper Insulation of Oil-Filled Power Transformers Using an ANFIS Algorithm

authored by

Firza Zulmi Rhamadhan, Tobias Kinkeldey, Peter Werle, Suwarno Suwarno

Abstract

The condition of the transformer insulation has an impact on the transformer's performance during the operation. The aging of the oil-impregnated cellulose insulation and the associated loss of mechanical strength are the important factors that limit the life of expectancy of a transformer. To determine the condition of the oil-impregnated cellulose insulation, the Degree of Polymerization (DP) parameter is commonly used. An Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed to predict the DP Value by the chemical characteristics and dissolved gas parameters (acidity, interfacial tension, CO, CO2, breakdown voltage, and water content of the oil). This paper generates some algorithms which are based on the input space partitioning method to generate rules (grid partition or subtractive clustering) and data is normalized or not. The estimation result has been observed and evaluated to provide that the ANFIS algorithm is suitable to estimate insulation condition on field operating transformers.

Organisation(s)

High Voltage Engineering and Asset Management Section (Schering Institute)

External Organisation(s)

Institut Teknologi Bandung (ITB)

Type

Conference contribution

Pages

202-207

No. of pages

6

Publication date

2021

Publication status

Published

Peer reviewed

Yes

ASJC Scopus subject areas

Energy Engineering and Power Technology, Electrical and Electronic Engineering, Control and Optimization

Electronic version(s)

https://doi.org/10.1109/CPEEE51686.2021.9383371 (Access: Closed)