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Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection
  • Universidade Estadual Paulista (UNESP)
  • Polytechnic Institute of Porto-IPP
  • Universidade de São Paulo (USP)
Research Executive Agency
Sponsorship Process Number: 
REA: 318912
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.
Issue Date: 
2013 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT LA 2013.
  • 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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Appears in Collections:Artigos, TCCs, Teses e Dissertações da Unesp

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