dc.contributor | Universidade Estadual Paulista (Unesp) | |
dc.contributor | Polytechnic Institute of Porto-IPP | |
dc.contributor | Universidade de São Paulo (USP) | |
dc.date.accessioned | 2014-05-27T11:30:15Z | |
dc.date.available | 2014-05-27T11:30:15Z | |
dc.date.created | 2014-05-27T11:30:15Z | |
dc.date.issued | 2013-08-26 | |
dc.identifier | 2013 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT LA 2013. | |
dc.identifier | http://hdl.handle.net/11449/76325 | |
dc.identifier | 10.1109/ISGT-LA.2013.6554383 | |
dc.identifier | WOS:000326589900015 | |
dc.identifier | 2-s2.0-84882308363 | |
dc.identifier | 9039182932747194 | |
dc.description.abstract | 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. | |
dc.language | eng | |
dc.relation | 2013 IEEE PES Conference on Innovative Smart Grid Technologies, ISGT LA 2013 | |
dc.rights | Acesso aberto | |
dc.source | Scopus | |
dc.subject | Charged System Search | |
dc.subject | Neural Networks | |
dc.subject | Nontechnical Losses | |
dc.subject | Charged system searches | |
dc.subject | Competitive environment | |
dc.subject | Meta-heuristic techniques | |
dc.subject | Multi-layer perceptron neural networks | |
dc.subject | Non-technical loss | |
dc.subject | Optimization techniques | |
dc.subject | Power distribution system | |
dc.subject | Trivial solutions | |
dc.subject | Electric load distribution | |
dc.subject | Electric utilities | |
dc.subject | Privatization | |
dc.subject | Smart power grids | |
dc.subject | Neural networks | |
dc.title | Multilayer perceptron neural networks training through charged system search and its Application for non-technical losses detection | |
dc.type | Actas de congresos | |