Publicação: Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection
Carregando...
Data
Orientador
Coorientador
Pós-graduação
Curso de graduação
Título da Revista
ISSN da Revista
Título de Volume
Editor
Tipo
Trabalho apresentado em evento
Direito de acesso
Acesso aberto
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 nature-inspired optimization techniques for training neural networks. Thus, it is possible to improve some applications on Smart Grids. © 2013 IEEE.
Descrição
Palavras-chave
Charged System Search, Neural Networks, Nontechnical Losses, Charged system searches, Competitive environment, Meta-heuristic techniques, Multi-layer perceptron neural networks, Non-technical loss, Optimization techniques, Power distribution system, Trivial solutions, Electric load distribution, Electric utilities, Privatization, Smart power grids, Neural networks
Idioma
Inglês
Como citar
2013 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT LA 2013.
