Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection

• Autor(a) principal: Pereira, Luis A. M. [UNESP]
• Data de Publicação: 2013
• Outros Autores: Afonso, Luis C. S. [UNESP], Papa, João Paulo [UNESP], Vale, Zita A., Ramos, Caio C. O., Gastaldello, Danillo S., Souza, André N.
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://dx.doi.org/10.1109/ISGT-LA.2013.6554383; http://hdl.handle.net/11449/76325
• topic: 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

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.