Title :
Hierarchical dynamic state estimator using ANN-based dynamic load prediction
Author :
Sinha, A.K. ; Mandal, J.K.
Author_Institution :
Dept. of Electr. Eng., Indian Inst. of Technol., Kharagpur, India
fDate :
11/1/1999 12:00:00 AM
Abstract :
The paper presents a new scheme for dynamic state estimation in power systems. The dynamics of the power system are modelled more realistically using artificial-neural-network-based bus-load prediction and load flow for state prediction. At the filtering step a hierarchical model which incorporates the measurement function nonlinearities is used. This considerably reduces the computational effort, making the proposed scheme suitable for on-line application. Test results for an IEEE 118-bus test system are presented to show the characteristics of the present method
Keywords :
load flow; load forecasting; neural nets; power system analysis computing; power system state estimation; ANN-based dynamic load prediction; IEEE 118-bus test system; artificial-neural-network-based bus-load prediction; computational effort; dynamic state estimation; filtering step; hierarchical dynamic state estimator; hierarchical model; load flow; measurement function nonlinearities; on-line application; power system dynamics modelling; short term load forecasting; state prediction;
Journal_Title :
Generation, Transmission and Distribution, IEE Proceedings-
DOI :
10.1049/ip-gtd:19990462
