Application of gene expression programming (GEP) in power transformers fault diagnosis using DGA

The diagnosis of incipient fault is very important for power transformer condition monitoring. The incipient faults are monitored by conventional and artificial intelligence (AI) based models. In this paper, the GEP has been utilized to identify the incipient faults in an oil-immersed power transformer. Its performance is compared with traditional IEC/IEEE and AI methods (i.e. ANN and fuzzy logic). The juxtaposition of fault classification of ANN and FL method notify that proposed approach is much swiftly. The desired test analysis of experimental data from working transformers in the Northern Power Grid of India has been executed to present the robustness of evaluated incipient faults for wide changes in operational and loading conditions perturbations.