Curvas típicas de carga para o planejamento operacional do sistema de distribuição. / Typical load curves for operational planning of distribution systems.

AUTOR(ES)
DATA DE PUBLICAÇÃO

2006

RESUMO

This dissertation presents and validate a load characterization framework for the operational planning of electric distribution networks based on characterization of customer typical load curves. The pattern recognition of typical load curves was based on the usage of self organizing maps, a type of neural network, over the huge database of customer field measurements performed by the electric energy utility for tariff review process, allowing the characterization of daily active energy consumption and power factor behaviors. The grouping module is based on self organizing map technique along with classic k-means technique, which proved to be an extremely effective tool for pattern recognition over large databases. The results of load estimation for 200 distribution feeders measured by SCADA system ensures the quality and accuracy of this framework which presents also an optimization model based on such measures, resulting significant improvement on load estimation for these feeders. This framework proves to be an effective tool for electric energy utilities for constant evaluation of customer’s power consumption behavior, allowing the maintenance of accurate estimations for operational planning of distribution networks.

ASSUNTO(S)

inteligência artificial typical load curves curvas típicas de carga electric energy distribution neural network artificial intelligence redes neurais distribuição de energia elétrica

ACESSO AO ARTIGO

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