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Integration of equation- and signal-based models in transient analysis of electric energy systems

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2 Author(s)
Saric, A.T. ; Dept. of Electr. & Comput. Eng., Northeastern Univ., Boston, MA, USA ; Stankovic, A.M.

The paper addresses analytical and practical aspects of integration of equation-based (classical) and signal-derived [artificial neural network (ANN)] dynamic models for transient analysis of large-scale dynamical systems. Our signal-based part is based on two ANNs, and is derived from measurements at boundary points. In this paper, we describe this hybrid modeling technique, and focus on: 1) a least square-based mechanism for on-line correction of dynamic variable predictions that is based on actual operating conditions; 2) the resilience of the algorithm to missing measurements due to failed communication links; and 3) a complete two-way interaction between the differential-algebraic equation based subsystem and the ANN-based subsystem. The paper demonstrates the feasibility of implementing our approach in standard power system software by integrating the ANN-based model with the transient analysis toolbox from Matlab. We illustrate capabilities of the proposed approach for transient analysis on a benchmark multi-machine example derived from the New England/New-York interconnected power system.

Published in:

Circuits and Systems I: Regular Papers, IEEE Transactions on  (Volume:53 ,  Issue: 7 )

Date of Publication:

July 2006

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