Determination of Power Transformer Fault’s Severity Based on Fuzzy Logic Model with GR, Level and DGA Interpretation

Abstract

Transformer defects are defined by their severity which is the intrinsic property of the transformer. Several approaches for identifying the severity of Power Transformer (PT) problems have previously been proposed; however, most published research does not incorporate Gas Level (GL), Gas Rate (GR), and DGA interpretation into a unified strategy. A novel technique in the form of fuzzy logic (FL) has been offered as a new way to assess faults’ severity by utilizing the combination of GL, GR, and DGA interpretation from the Duval Pentagon Method (DPM) to increase the reliability of the faults’ severity evaluation of PT. Based on the local population, a four-level typical concentration and rate were created. A Deep Learning (DL) oriented Convolutional Neural Network (CNN) based DPM and Harris Hawks Optimization (HHO) method with a high agreement to that same graphical DPM has also been devised to enable the evaluation of hundreds of PT information easy. The proposed method was applied to 448 PTs, and it was then used to assess the severity of problems in PTs using historical DGA data. Due to the integration of GL, GR, and DGA interpretation results in one technique, this novel strategy yields good agreement with earlier methods, but with better sensitivity.