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Multilayer Perceptron Neural Networks Training Through Charged System Search and its Application for Non-Technical Losses Detection
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Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Charged System Search Neural Networks Nontechnical Losses
Contexto Educativo
Citação
Editora
IEEE