Utilize este identificador para referenciar este registo: http://hdl.handle.net/10400.22/5878
Título: Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection
Autor: Pereira, Luis
Afonso, Luis
Papa, João
Vale, Zita
Ramos, Caio
Gastaldello, Danilo
Souza, André
Palavras-chave: Charged System Search
Neural Networks
Nontechnical Losses
Data: Abr-2013
Editora: IEEE
Relatório da Série N.º: IEEE PES;2013
Resumo: The non-technical loss is not a problem with trivial solution or regional character and its minimization represents the guarantee of investments in product quality and maintenance of power systems, introduced by a competitive environment after the period of privatization in the national scene. In this paper, we show how to improve the training phase of a neural network-based classifier using a recently proposed meta-heuristic technique called Charged System Search, which is based on the interactions between electrically charged particles. The experiments were carried out in the context of non-technical loss in power distribution systems in a dataset obtained from a Brazilian electrical power company, and have demonstrated the robustness of the proposed technique against with several others natureinspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids.
URI: http://hdl.handle.net/10400.22/5878
DOI: 10.1109/ISGT-LA.2013.6554383
Versão do Editor: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?tp=&arnumber=6554383&queryText%3DMultilayer+Perceptron+Neural+Networks+Training+Through+Charged+System+Search+and+its+Application+for+Non-Technical+Losses+Detection
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