Open AccessFaults diagnosis and assessment of transformer insulation oil quality: intelligent methods based on dissolved gas analysis a-review
Author(s) -
Ahmed Raisan,
Maslina Yaacob,
Malik Abdulrazzaq Alsaedi
Publication year - 2015
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v4i1.3941
Subject(s) - dissolved gas analysis , transformer , reliability engineering , transformer oil , engineering , artificial neural network , fuzzy logic , expert system , forensic engineering , process engineering , petroleum engineering , computer science , artificial intelligence , electrical engineering , voltage
The search for determining accurate faults and assessing the oil quality of high voltage electrical power transformers for life-long maintenance is ever-demanding. The durability of transformers function is significantly decided by the excellence of its insulation which deteriorates over time due to temperature fluctuations and moisture contents. The accurate diagnoses of faults in early stages and the efficient assessment of oil quality using an intelligent program is the key challenges in protecting transformers from potential failures occur during operation to avoid economic losses. The dissolved gases analysis in oil is a reliable method in the diagnosis of faults and assessing the quality of insulating oil in transformers. Recently, application of artificial intelligence (AI) has included fuzzy logic, expert system (EPS), and artificial neural network (ANN), Expert system and fuzzy logic can take DGA standards. This paper represents the review most of the methods used to diagnose faults and assessment of insulating oil for transformers through the dissolved gases analysis DGA.