DOI:
https://doi.org/10.14483/23448393.23305Publicado:
2026-06-15Número:
Vol. 31 Núm. 1 (2026): Enero-abrilSección:
Ingeniería Eléctrica, Electrónica y TelecomunicacionesClassification 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
Clasificación de las señales transitorias producidas por el impacto directo de un rayo en una red de media tensión en el dominio de la frecuencia mediante una red neuronal de aprendizaje profundo
Palabras clave:
Artificial intelligence, lightning, polarized media, frequency domain, deep neural networks (en).Palabras clave:
inteligencia artificial, descarga eléctrica atmosférica, medios polarizados, dominio de la frecuencia, redes de aprendizaje profundo (es).Descargas
Resumen (en)
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.
Resumen (es)
Contexto: Esta investigación presenta dos resultados principales: (i) la aplicación de redes de aprendizaje profundo para la clasificación de sobretensiones transitorias en el dominio de la frecuencia y (ii) la formulación fisicomatemática para el acoplamiento de descargas eléctricas atmosféricas en medios polarizados en el dominio de la frecuencia.
Método: Inicialmente, se obtuvieron registros de sobretensiones transitorias producto de descargas eléctricas atmosféricas, a partir de simulaciones en el software EMTP-ATP, tomando como referencia la red de distribución IEEE de 13 nodos. Las sobretensiones transitorias fueron transformadas del dominio del tiempo al dominio de la frecuencia mediante la aplicación de la transformada rápida de Fourier. Posteriormente, se entrenaron las redes neuronales y se compararon sus resultados. Para descargas eléctricas por impacto indirecto, se realizó un manejo en variable compleja de la permitividad y la permeabilidad, las cuales fueron aplicadas al modelo de acoplamiento de rayos en líneas o redes de distribución.
Resultados: Se alcanzó una exactitud de 80 y 89.99 % en la clasificación de sobretensiones transitorias para las redes neuronales GoogLeNet y Python-ChatGPT respectivamente. Por otra parte, este trabajo presenta la expresión de acoplamiento de descargas eléctricas atmosféricas en medios polarizados.
Conclusiones: La clasificación de sobretensiones transitorias mediante redes neuronales de capa profunda y la expresión fisicomatemática de acoplamiento del rayo en el dominio de la frecuencia mediante variables complejas permite ampliar la comprensión de la respuesta de la red ante este tipo de fenómenos naturales.
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