A novel fuzzy approach for winding paper insulation health monitoring of oil‐immersed power transformer using non‐destructive failure parameters

Abstract Power transformers, a significant and critical component of power system, require regular condition monitoring by the utilities for uninterrupted power supply. The kraft paper made of cellulose is used as the solid insulation of the transformer which undergoes thermal, electrical and environmental stresses causing its progressive ageing. It thereby reduces the physical strength of the paper and ultimately affects the transformer's remaining useful life (RUL). The current state (physical) of the cellulosic (solid) insulating paper is predicted in this article using the fuzzy inference system (FIS) based RUL assessment technique. Three non‐destructive parameters, that is, 2‐Furfuraldehyde (2‐FAL), Carbon Monoxide (CO) and Carbon dioxide (CO 2 ) are used to determine the value of the Degree of Polymerisation (i.e. DP) which directly reflects the current physical state of the insulating material. Four states (Healthy, Poor, Very Poor, and Worst) of the insulating paper are identified based on the values of DP. Five FIS models have been developed separately with different membership functions and a number of fuzzy if‐then rules are formulated to estimate the value of DP. Every model is tested with 30 test samples to validate the system accuracy. The physical condition of the insulation estimated using FIS built with trapezoidal membership function has been found to be considerably precise with an average absolute deviation of 0.33%. Hence, a comprehensive assessment of transformer's health is carried out with the prominent non‐destructive parameters.

Authors

Institutions

Publication Details

Journal
IET Electric Power Applications
Published
2024-12-26
DOI
https://doi.org/10.1049/elp2.12533
Citations
1
Primary Topic
Power Transformer Diagnostics and Insulation
Type
article
Field-Weighted Citation Impact
0.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A novel fuzzy approach for winding paper insulation health monitoring of oil‐immersed power transformer using non‐destructive failure parameters

Asyraf Afthanorhan, Shakeb A. Khan, Hythem Hashem, Fausto Pedro Garcı́a Márquez et al.
1 citations
IET Electric Power Applications
Power Transformer Diagnostics and Insulation
0.21
article

A novel fuzzy approach for winding paper insulation health monitoring of oil‐immersed power transformer using non‐destructive failure parameters

Asyraf Afthanorhan, Shakeb A. Khan, Hythem Hashem, Fausto Pedro Garcı́a Márquez, Hasmat Malik, Md. Danish Equbal, Md. Manzar Nezami, Mohammad Asef Hossaini
article en
1 citations

Abstract

Abstract Power transformers, a significant and critical component of power system, require regular condition monitoring by the utilities for uninterrupted power supply. The kraft paper made of cellulose is used as the solid insulation of the transformer which undergoes thermal, electrical and environmental stresses causing its progressive ageing. It thereby reduces the physical strength of the paper and ultimately affects the transformer's remaining useful life (RUL). The current state (physical) of the cellulosic (solid) insulating paper is predicted in this article using the fuzzy inference system (FIS) based RUL assessment technique. Three non‐destructive parameters, that is, 2‐Furfuraldehyde (2‐FAL), Carbon Monoxide (CO) and Carbon dioxide (CO 2 ) are used to determine the value of the Degree of Polymerisation (i.e. DP) which directly reflects the current physical state of the insulating material. Four states (Healthy, Poor, Very Poor, and Worst) of the insulating paper are identified based on the values of DP. Five FIS models have been developed separately with different membership functions and a number of fuzzy if‐then rules are formulated to estimate the value of DP. Every model is tested with 30 test samples to validate the system accuracy. The physical condition of the insulation estimated using FIS built with trapezoidal membership function has been found to be considerably precise with an average absolute deviation of 0.33%. Hence, a comprehensive assessment of transformer's health is carried out with the prominent non‐destructive parameters.

IET Electric Power ApplicationsVol. 19(1)
Galgotias University (IN), University of Bisha (SA), University of Technology Malaysia (MY), Sultan Zainal Abidin University (MY), Jamia Millia Islamia (IN), Graphic Era University (IN), University of Castilla-La Mancha (ES), GLA University (IN)
GLA University, Jamia Millia Islamia, Universiti Teknologi Malaysia, Universiti Sultan Zainal Abidin
Openalex Percentile: Top 44%
Power Transformer Diagnostics and Insulation
0.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.