Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm\nfor many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely\ndissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in\ntransformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity\nthrough use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault\nformation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the\ncalculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based\nkey gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil\nsamples drawn from transformers of different specifications and age are used to validate the model. Model results show that\ncorrectly detecting fault type and its severity determination based on total energy released during faults can enhance decisionmaking\nin prioritizing maintenance of faulty transformers.
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