Nonlinear power system model reduction based on empirical Gramians

Zhao, Hongshan; Lan, Xiaoming; Ren, Hui

doi:10.1515/jee-2017-0077

Abstract

An effective nonlinear model reduction approach, empirical Gramians balanced reduction approach, is studied, to reduce the computation complexity in nonlinear power system model application. The realization procedure is: firstly, computing the empirical controllable and observable Gramians matrices of nonlinear power system model, secondly, by these two matrices, computing the balance transformation matrix to obtain the balanced system model of the original model, then, computing the controllable and observable matrices of the balanced system to obtain the diagonal Hankel singular matrix. Finally, deciding the lower-order subspace to obtain the reduced power system model. A 15-machine power system model is taken as an example to perform the reduction simulation analysis.

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Nonlinear power system model reduction based on empirical Gramians

Abstract

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