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Electric power system static state estimation through Kalman filtering and load forecasting

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3 Author(s)
Blood, E.A. ; Electr. & Comput. Eng. Dept., Carnegie Mellon Univ., Pittsburgh, PA ; Krogh, B.H. ; Ilic, M.D.

Static state estimation in electric power systems is normally accomplished without the use of time-history data or prediction. This paper presents preliminary work on the use of the discrete-time Kalman filter to incorporate time history and power demand prediction into state estimators. The problem of state estimation combined with the knowledge of the forecasted load is posed as a Kalman filtering problem using a novel discrete-time model. The model relates current and previous states using the electric power flow equations. An IEEE 14-bus test system example is used to illustrate the potential for enhanced performance of such Kalman filter-based state estimation.

Published in:

Power and Energy Society General Meeting - Conversion and Delivery of Electrical Energy in the 21st Century, 2008 IEEE

Date of Conference:

20-24 July 2008