| Texto completo | |
| Autor(es): |
Moreira, Alexandre C.
[1]
;
Paredes, Helmo K. M.
[2]
;
de Souza, Wesley A.
[3]
;
Nardelli, Pedro H. J.
[4]
;
Marafao, Fernando P.
[2]
;
da Silva, Luiz C. P.
[3]
Número total de Autores: 6
|
| Afiliação do(s) autor(es): | [1] Fed Univ Sao Joao Del Rei UFSJ, Telecommun & Mech Engn Dept DETEM, Rodovia MG 443, KM 7, BR-36420000 Ouro Branco, MG - Brazil
[2] Sao Paulo State Univ Unesp, Inst Sci & Technol, Av Tres Marco 511, BR-18087180 Sorocaba, SP - Brazil
[3] Univ Campinas UNICAMP, Sch Elect & Comp Engn FEEC, Dept Energy & Syst DSE, Av Albert Einstein 400, BR-13083970 Campinas, SP - Brazil
[4] Univ Oulu, CWC, Erkki Koiso Kanttilan Katu 3, FIN-90570 Oulu - Finland
Número total de Afiliações: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | JOURNAL OF CONTROL AUTOMATION AND ELECTRICAL SYSTEMS; v. 29, n. 1, p. 75-90, FEB 2018. |
| Citações Web of Science: | 2 |
| Resumo | |
This paper assesses different applied pattern recognition algorithms to decide the most appropriate power factor compensator for a particular point of common coupling. Power factor, current unbalance factor, total demand distortion, voltage harmonic distortion and reactive power daily variation, as well as human expertise, are the key parameters used to set each recognition algorithm. These algorithms are then trained with a series of both simulation and experimental data. Numerical results consistently indicate the decision-tree algorithm with depth 20 as the best classifier for power factor improvement in terms of all metrics considered in this work. (AU) | |
| Processo FAPESP: | 16/08645-9 - Pesquisas interdisciplinares em redes inteligentes de energia elétrica |
| Beneficiário: | João Bosco Ribeiro do Val |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |