TY  - CONF
TI  - Novel Hybrid Algorithm for Investigation of Dissolved Gas Analysis for Alternate Dielectric Fluids of Power Transformer
Y1  - 2025///
A1  - Soni, Rahul
A1  - Raj, Raymon Antony
A1  - Silwal, Bishal
A1  - Prasojo, Rahman Azis
A1  - Darwish, M.M.F.
A1  - Bakar, Norazhar Abu
PB  - Institute of Electrical and Electronics Engineers Inc.
AV  - none
N1  - Cited by: 1
N2  - The power transformer is the most important element of equipment in an electrical transmission and distribution network. The power and voltage levels that these transformers can manage determine their size, and as the transformer's volume increases, internal isolation and cooling become more challenging. The most popular insulating fluid used in transformers for insulation and cooling is mineral-based oil, which has demonstrated exceptional dielectric and thermal qualities. However, alternative oil technologies have been sought to replace mineral-based oils in recent years because to concerns about safety, cost, and environmental impact. In this context, extensive research efforts have emerged on biological oils for use in transformers. This paper examines the sustainability, safety, and electrical performance of bio-based ester oil in transformer applications with respect to dissolved gas analysis (DGA). AI-based Fuzzy logic controller (FLC) with Duval's triangle (DT) and Duval's pentagon (DP), IEC, CIGRE reports are used in a hybrid manner to identify the possible incipient faults inside the oil-immersed transformers. The graphical analysis of various fuzzy logic plots along with DT and DP are utilized to critically analyze the possible incipient faults arrayed due to DGA. The proposed work proficient compared to previously published results. Highest efficiency of DT and DP based FLC (92), followed by furan analysis (82.3). Lastly, recommendations are made on potential fixes for the problems raised and the future course of study for alternate dielectric fluids-based power transformers. The suggested techniques in this research offer a higher diagnostic accuracy than the present approach. © 2025 IEEE.
SN  - 979-833154210-8
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-105004644003&doi=10.1109%2fSSDEE64538.2025.10968045&partnerID=40&md5=f2fea119f0dc6cbfd362a28f7a7b3980
KW  - Distribution transformers; Effluents; Electric autotransformers; Electric instrument transformers; Electric transformer insulation; Exhaust gases; Alternate dielectric fluid; Dielectric fluid; Dissolved gases analysis; Electrical transmission and distributions; Elements of equipment; Fuzzy logic controllers; Hybrid algorithms; Incipient faults; Power; Power levels; Power transformers
ID  - scholars20450
ER  -