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A Generalized Approach for Computing Most Sensitive Eigenvalues With Respect to System Parameter Changes in Large-Scale Power Systems

The recently-developed Two-sided Arnoldi and Sensitive Pole Algorithm (TSA-SPA) is effective and robust in computing the most sensitive eigenvalues with respect to control parameter changes in large-scale power systems. This paper extends the TSA-SPA to handle different system parameters, including control, system operating and¬†network¬†parameters. The proposed algorithm makes use of perturbation in reduced matrix obtained from Arnoldi/TSA method through linearization and successfully avoids the need for TSA-SPA to formulate the whole state matrix of the system and to explicitly calculate the elements’ variations in system state matrix.

A new deflation method is also proposed and adopted in the generalized algorithm to find other sensitive eigenvalues. Simulation results illustrate that the generalized algorithm is able to not only maintain the excellent properties of TSA-SPA in terms of convergence and robustness, but also consider various parameter changes effectively in large-scale power systems.