Open AccessDeep Machine Learning-based Asset Management Approach for Oil-Immersed Power transformers using Dissolved Gas Analysis
Author(s) -
Lan Jin,
Dowon Kim,
Kit Yan Chan,
Ahmed Abu-Siada
Publication year - 2024
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2024.3366905
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Reliable operation of oil-immersed power transformers is crucial for electrical transmission and distribution networks. However, the aging of high voltage assets including power transformers along with the increasing of load demand have heightened the importance of adopting cost-effective asset management strategies. Dissolved gas analysis (DGA) has been recognized as a valuable diagnostic tool for detecting potential faults and monitoring the condition of oil-immersed power transformers. Traditional offline DGA method involves periodic sampling and laboratory analysis, which often results in delayed detection and response to emerging faults. To address these limitations, online DGA approach has been emerged to provide real-time monitoring and continuous data acquisition. This paper presents a new asset management approach for mineral oil-immersed power transformers by analysing the online DGA data using convolutional neural networks. The proposed approach provides real time solutions to classify emerging fault type and predict transformer health deterioration level with high accuracy. Results show that the accuracy of fault diagnostics of the proposed approach is approximately 87%.