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Nonlinear parameter neuro-estimation for optimal tuning of power system stabilizers

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2 Author(s)
Seung-Monk Back ; Sch. of Electr. & Electron. Eng., YONSEI Univ., Seoul ; Jung-Wook Park

This paper describes nonlinear parameter estimation of non-smooth nonlinear device by using a feed-forward neural network (FFNN) embedded in a hybrid system modeling. The hybrid systems are modeled by the differential-algebraic-impulsive-switched (DAIS) structure. In a switched linear hybrid system, the FFNN is applied to identify full dynamics of an objective function J formed by the states. Moreover, the partial derivatives of function J with respect to the each state are approximated by the computation of the backpropagation through the FFNN. Then, this paper focuses on the FFNN based estimator for the non-smooth nonlinear dynamic behaviors due to saturation limiter of the power system stabilizer (PSS) in both a single machine infinite bus (SMIB) system and a multi-machine power system (MMPS).

Published in:

Industrial Informatics, 2008. INDIN 2008. 6th IEEE International Conference on

Date of Conference:

13-16 July 2008