Title :
Preserving data redundancy in state estimation through a predictive database
Author :
Filho, M. B Do Coutto ; Souza, J.C.S. ; Matos, R.S.G. ; Schilling, M.Th.
Author_Institution :
Appl. Comput. & Autom., Fluminense Fed. Univ., Rio de Janeiro, Brazil
Abstract :
This paper presents strategies for preserving data redundancy in state estimation through forecasting-aided state estimators (FASE). Forecasted states/measurements are obtained by an artificial neural network-based model. Many aspects of the pseudomeasurement provision problem are considered, regarding the use of forecasted measurements. The following questions emerge naturally. Numerical results covering the application of the proposed strategies under different levels of redundancy deterioration are presented and discussed.
Keywords :
neural nets; power system analysis computing; power system state estimation; redundancy; artificial neural network; data redundancy preservation; forecasting-aided state estimators; predictive database; pseudomeasurement provision; redundancy deterioration; state estimation; Automatic control; Control systems; Data acquisition; Data processing; Databases; Observability; Power system analysis computing; Power system reliability; Redundancy; State estimation;
Conference_Titel :
Electric Power Engineering, 1999. PowerTech Budapest 99. International Conference on
Conference_Location :
Budapest, Hungary
Print_ISBN :
0-7803-5836-8
DOI :
10.1109/PTC.1999.826703
