Title :
Layer Recurrent Neural Network Solution for an Electromagnetic Interference Problem
Author :
Micu, Dan D. ; Czumbil, Levente ; Christoforidis, Georgios ; Ceclan, Andrei
Author_Institution :
Electr. Eng. Dept., Tech. Univ. of Cluj Napoca, Cluj-Napoca, Romania
fDate :
5/1/2011 12:00:00 AM
Abstract :
The paper presents an original contribution related to the implementation of a neural network artificial intelligence (AI) technique through Matlab environment, on the study of induced AC voltage in the underground metallic pipeline, due to nearby high voltage grids. The advantage yields in a simplified computation model compared to FEM, and implicitly a lower computational time. In comparison with other neural network solutions identified in the literature, where the induced AC potential is directly evaluated, the authors of this paper propose a new neural network solution to evaluate MVP on the studied domain, using a larger training database for a large panel of different geometries.
Keywords :
electromagnetic interference; learning (artificial intelligence); mathematics computing; power engineering computing; power grids; recurrent neural nets; underground cables; MVP; Matlab environment; artificial intelligence technique; computation model; electromagnetic interference problem; high voltage grids; induced AC potential; induced AC voltage; layer recurrent neural network solution; magnetic vector potential; training database; underground metallic pipeline; Artificial neural networks; Conductors; Electric potential; Finite element methods; Pipelines; Testing; Training; Electromagnetic compatibility; electromagnetic fields; finite element method; neural networks; pipelines; transmission lines;
Journal_Title :
Magnetics, IEEE Transactions on
DOI :
10.1109/TMAG.2010.2091494