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

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2013-08-26

Autores

Pereira, Luis A. M. [UNESP]
Afonso, Luis C. S. [UNESP]
Papa, João Paulo [UNESP]
Vale, Zita A.
Ramos, Caio C. O.
Gastaldello, Danillo S.
Souza, André N.

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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.

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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

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2013 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT LA 2013.