Reconhecimento de padrões em sistemas de energia elétrica através de uma abordagem geométrica aprimorada para a construção de redes neurais artificiais

Abstract

This work is based on the method of successive geometric segmentations (SGSM) for the construction of an artificial neural network capable of generating both the network topology as the weight of neurons without specifying initial parameters. The MSGS allows to identify a set of hyperplanes in the Rn space that when properly combined, can separate two or more data classes. Specifically in this work is used an improvement to SGSM based on kernel density estimates (KDE). Using KDE, it is possible to find new hyperplanes of separation more consistently and, from there, lead to data classification with accuracy rates higher than originally technique. In this paper, the improved SGSM is first used satisfactorily to identify patterns in electrical power systems. The method has been adjusted to the classification of incipient faults in power transformers and the results have achieved rates above related work. The improved SGSM has also been adapted to classify and locate inter-circuit faults on double circuit overhead transmission lines with positive results compared with the scientific literature.