On the Application of Artificial Neural Network for Classification of Incipient Faults in Dissolved Gas Analysis of Power Transformers

Abstract

Oil-submerged transformer is one of the inherent instruments in the South African power system. Transformer malfunction or impairment may interpose the operation of the electric power distribution and transmission system, coupled with liability for high overhaul costs. Hence, recognition of inchoate faults in an oil-submerged transformer is indispensable and it has turned into an intriguing subject of interest by utility owners and transformer manufacturers. This work proposes a hybrid implementation of a multi-layer artificial neural network (MLANN) and IEC 60599:2022 gas ratio method in identifying inchoate faults in mineral oil-based submerged transformers by employing the dissolved gas analysis (DGA) method. DGA is a staunch practice to discover inchoate faults as it furnishes comprehensive information in examining the transformer state. In current work, MLANN was established to pigeonhole seven fault types of transformer states predicated on the three International Electrotechnical Commission (IEC) combustible gas ratios. The designs enmesh the development of numerous MLANN algorithms and picking networks with the optimum performance. The gas ratios are in accordance with the IEC 60599:2022 standard whilst an empirical databank comprised of 100 datasets was used in the training and testing activities. The designated MLANN design produces an overall correlation coefficient of 0.998 in the categorization of transformer state with reference to the combustible gas produced.