Intelligent Interpretation of Frequency Response Signatures to Diagnose Radial Deformation in Transformer Windings Using Artificial Neural Network

Abstract

Transformers are prominent elements of an electrical grid in which continuous service is of great importance, and high reliability of the entire network depends on health condition of the transformers. Transformer windings are susceptible to mechanical tensions as a result of poor operation or transit. Radial deformation (RD) as a mechanical winding defect exerts disruptive influences on the performance of the transformer. With regard to transformer monitoring, frequency response analysis (FRA) has established itself as a reliable diagnostic tool. Nonetheless, complexity and open questions surround the decipherment of FRA results because reliable interpretation code is unavailable. This study presents an artificial intelligence-based code for interpreting frequency response traces. In this study, RD defects are practically implemented on the windings of a 1600 kVA transformer operating at 20 kV. Practical measurements are taken of FRA traces, and then feature vectors are extracted using adequate and sensitive numerical indexes, including cross-correlation factor (CCF), normalized root mean square deviation (NRMSD), Lin's concordance coefficient (LCC), and fitting percentage (FP). Every one of the frequency response parts, which are magnitude, angle, imaginary, and real parts, is investigated. In addition, an artificial neural network (ANN) regarded as an intelligent classifier employing extracted features to distinguish locations of RD defects. The proposed intelligent network's final performance assessment was conducted employing a cross-validation technique named K-fold. The most appropriate numerical index beside the most effective part of the frequency response are introduced.