| Texto completo | |
| Autor(es): |
Carvalho, Breno
;
Bretas, Newton
;
Bretas, Arturo
;
IEEE
Número total de Autores: 4
|
| Tipo de documento: | Artigo Científico |
| Fonte: | 2017 IEEE POWER & ENERGY SOCIETY GENERAL MEETING; v. N/A, p. 5-pg., 2017-01-01. |
| Resumo | |
This paper introduces a methodology for network parameter error detection, identification and correction based on gross error analysis. The presented methodology is built on the Weighted Least Square (WLS) state estimator (SE) and a local state vector augmentation technique for parameter estimation. In order to keep a safe redundancy level, only suspicious parameters are included in the augmented estimation process. The parameter error detection is made through a Chi-square (chi(2)) Hypothesis Testing (HT) applied to the proposed Composed Measurement Error (GAIL). Once detected, the parameter error identification is made through the Largest Normalized Error Test (LNET) property. Once identified, the suspicious parameter is added to the state vector and a simultaneous state/parameter estimation is performed in order to correct the parameter value. Finally, the correction of parameter errors on the network database is performed. One should be aware that this methodology can be applied for single and multiple simultaneous parameter error scenarios. Validation of the proposed methodology is made on the IEEE 57-bus system. Test results highlights method's increased reliability and robustness and its easy implementation enhances method's potential for real power systems applications. (AU) | |
| Processo FAPESP: | 15/15274-4 - Estimação de estado em sistemas elétricos de potência: a interpretação geométrica aplicada ao processamento de erros topológicos |
| Beneficiário: | Breno Elias Bretas de Carvalho |
| Modalidade de apoio: | Bolsas no Exterior - Estágio de Pesquisa - Doutorado |