Ensuring a stable and efficient transformer operation is a very crucial task nowadays, especially with the integration of modern and sensitive electrical equipment and appliances down the line. However, transformer maloperation still cannot be completely avoided, particularly with the existence of inrush current that possess similar characteristics as the fault currents when a fault occurred. Thus, this paper proposes an enhanced method for inrush current identification based on a backpropagation (BP) network, optimized using genetic and simulated annealing algorithms. The proposed method has the ability to find the global optimal solution while avoiding local optima, with increased solution accuracy and low calculation complexity. Through extensive simulations, it was found that the inrush and fault currents have differences in their harmonic contents, which can be exploited for the identification of those currents using the proposed identification method. The proposed genetic simulated annealing−BP (GSA-BP) algorithm make use of 200 current samples to improve the detection accuracy of the inrush current from 80% to 97.5%. Comparative studies performed against the existing identification methods show that the GSA-BP network has superior efficiency and accuracy while being practical for real-life application to improve the transformer protection system.