Abstract
Decision making on transformer insulation condition based on the evaluated incipient faults and aging stresses has been the norm for many asset managers. Despite being the extensively applied methodology in power transformer incipient fault detection, solely dissolved gas analysis (DGA) techniques cannot quantify the detected fault severity. Fault severity is the core property in transformer maintenance rankings. This paper presents a fuzzy logic methodology in determining transformer faults and severity through use of energy of fault formation of the evolved gasses during transformer faulting event. Additionally, the energy of fault formation is a temperature-dependent factor for all the associated evolved gases. Instead of using the energy-weighted DGA, the calculated total energy of related incipient fault is used for severity determination. Severity of faults detected by fuzzy logic-based key gas method is evaluated through the use of collected data from several in-service and faulty transformers. DGA results of oil samples drawn from transformers of different specifications and age are used to validate the model. Model results show that correctly detecting fault type and its severity determination based on total energy released during faults can enhance decision-making in prioritizing maintenance of faulty transformers.
Highlights
Power transformers are crucial equipment for viable and dependable performance of a power system
Since it was difficult to deduce the inaccuracies of distinct instruments and human errors of each data set from different sources, a 95% confidence level was assumed to cater for these uncertainties
The proposed fault severity determination mathematical model was established upon the decomposition of crude oil in which eicosane (C20H42) was used as the starting decomposition material. us, the evolved gases enthalpy change of reaction was arrived at using this proposed decomposing product. e inputs to the fuzzy model were the concentration of the seven key gases evolved within the transformer insulation system
Summary
Power transformers are crucial equipment for viable and dependable performance of a power system. A fuzzy logic fault detection model is developed based on the seven key gases (DGA) interlinked with total energy involved in the faulting process. In DGA-based diagnostics, there are methods which can diagnose faults accurately with fewer number of gases, like three gases in Duval triangles or pentagons, this study adopts the seven key gases approach mainly to impact on quantifying accurately the severity of the detected faults which involves these characteristic key gases. The proposed fuzzy-DGA model can detect high arcing fault energy, but at the same time, the insulation is experiencing thermal fault and its severity is shown by significant amount of its oil thermal faulting energy, and it signifies that the transformer is experiencing multiple faults in which its severity can be quantified well by energy of fault approach. Depending on the amount of energy involved in the faulting process, the asset managers can judge whether to maintain the transformer on-line or off-line depending on the criticality of the faults
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