Classification of Transient Signals Produced by Direct Impacts of a Lightning Strike on a Medium Voltage Network in the Frequency Domain Using a Deep Learning Neural Network - Perdomo Orjuela, Luis Eduardo; Santamaria, Francisco; Vera, Nelson Enrique (Ingeniería, 2026)
Context: This research presents two main results: (i) the application of deep learning networks for the classification of transient overvoltage in the frequency domain, and (ii) the physical-mathematical formulation for the coupling of atmospheric electrical discharges in polarized media in the frequency domain. Method: Initially, transient overvoltage records resulting from atmospheric electrical discharges were obtained from simulations in EMTP-ATP software, using the IEEE 13-node distribution network as a reference. Each transient overvoltage was transformed from the time domain to the frequency domain via the Fast Fourier Transform. Subsequently, the neural networks were trained, and their results were compared. For indirect lightning strikes, a complex-variable analysis of permittivity and permeability was performed. These variables were applied to the lightning coupling model in distribution lines or networks. Results: Classification accuracies of 80 and 89.99% were achieved for transient overvoltage using the GoogLeNet and Python-ChatGPT neural networks, respectively. Furthermore, this work presents the coupling expression for atmospheric electrical discharges in polarized media. Conclusions: The classification of transient overvoltage records using deep-layer neural networks and the physical-mathematical expression for lightning coupling in the frequency domain through complex variables allows for a broader understanding of the network response to this type of natural phenomenon.