Machine learning-based identification of cellulose particle pre-bridging and bridging stages in transformer oil

Mat Daud, Marizuana and Ahmad Mustafa, Nur Badariah and Zainuddin, Hidayat and Nik Ali, Nik Hakimi and Nor Rashid, Fadilla Atyka (2025) Machine learning-based identification of cellulose particle pre-bridging and bridging stages in transformer oil. International Journal of Advanced Computer Science and Applications, 16 (3). 373 - 382. ISSN 2158-107X

Text 0075804092025153459.pdf Available under License Creative Commons Attribution. Download (945kB)

Abstract

The deterioration of transformer oil quality is influenced by factors including the presence of acids, water, and other contaminates such as cellulose particles and metal dust. The dielectric strength of the oil decreases over time and depending on the service conditions. This study introduces an efficient machine learning method to classify the pre-bridging and bridging stages by analyzing the formation of cellulose particle bridges in synthetic ester transformer oil. It is important to note that the prebridging and bridging stages indicate a pre-breakdown condition. The machine learning approach implements the combination of digital image processing (DIP) technique and support vector machine (SVM). The DIP technique, specifically the feature extraction method, captures the feature descriptors from the cellulose particles bridging images including area, MajorAxisLength, MinorAxisLength, orientation, contrast, correlation, homogeneity and energy. These descriptors are used in SVM to assess the pre-bridging and bridging stages in transformer oil without human intervention. Various SVM models were implemented, including linear, quadratic, cubic, fine Gaussian, medium Gaussian, and coarse Gaussian. The results achieved 96.5% accuracy using quadratic and cubic SVM models with the eight feature descriptors. This research has significant implications, allowing early detection of transformer breakdown, prolonging transformer lifespan, ensuring uninterrupted power plant operations, and potentially reducing replacement costs and electricity disruptions due to late breakdown detection.

Item Type:	Article
Uncontrolled Keywords:	Cellulose bridging, Feature classification, Feature extraction, Oil deterioration, Support vector machine, Synthetic transformer oil
Divisions:	Faculty Of Electrical Technology And Engineering
Depositing User:	Norfaradilla Idayu Ab. Ghafar
Date Deposited:	30 Dec 2025 06:32
Last Modified:	30 Dec 2025 06:32
URI:	http://eprints.utem.edu.my/id/eprint/29354
Statistic Details:	View Download Statistic

Actions (login required)

View Item