Resumo

ARANTES MONTEIRO, Raul Vitor et al. Training algorithms evaluation for artificial neural network to temporal prediction of photovoltaic generation. Energética [online]. 2016, vol.37, n.3, pp. 218-228. ISSN 1815-5901.

polluting technologies, mainly those using renewable sources, to distribution networks. Hence, it becomes increasingly important to understand technical challenges, facing high penetration of PV systems at the grid, especially considering the effects of intermittence of this source on the power quality, reliability and stability of the electric distribution system. This fact can affect the distribution networks on which they are attached causing overvoltage, undervoltage and frequency oscillations. In order to predict these disturbs, artificial neural networks are used. This article aims to analyze 3 training algorithms used in artificial neural networks for temporal prediction of the generated active power thru photovoltaic panels. As a result it was concluded that the algorithm with the best performance among the 3 analyzed was the Levenberg-Marquadrt

Palavras-chave : training algorithm; artificial neural networks; photovoltaic penetration; microgeneration; power quality.

        · resumo em Português | Espanhol     · texto em Português     · Português ( pdf )