Nonlinear power system model reduction based on empirical gramians

Abstract

An effective nonlinear model reduction approach, empirical Gramians balanced reduction approach, is studied, in order to reduce the computation complexity in nonlinear power system model application. The realization procedure is: firstly, computing the empirical controllable and observable Gramians matrices, secondly, computing the balance transformation matrix to obtain the balanced system model of the original model, then, and obtaining the diagonal Hankel singular values of balance transformation matrix. Finally, deciding the lower-order subspace to obtain the reduced model. A 15-machine nonlinear power system model is taken as an example to perform the reduction simulation analysis. Simulation result shows that, under the condition to maintain the stability and input-output dynamic behavior of the original system, the order of the reduced system model could be as low as 1/5 of that of the original system model, verifying the effectiveness of the proposed approach.