Evaluation and identification of lightning models by artificial neural networks
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da Silva, Ivan Nunes [UNESP]
de Souza, Andre Nunes [UNESP]
Bordon, Mario Eduardo [UNESP]
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This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.
Atmospheric humidity, Computer simulation, Electric fields, Electric potential, Lightning, Mathematical models, Pressure effects, Thermal effects, Waveform analysis, Critical disruptive voltage, Electrical field intensity, Neural networks
Proceedings of the International Joint Conference on Neural Networks, v. 6, p. 3816-3820.