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 strong in computing the foremost sensitive eigenvalues with respect to control parameter changes in large-scale power systems. This paper extends the TSA-SPA to handle completely different system parameters, together with 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 full state matrix of the system and to explicitly calculate the elements' variations in system state matrix. A brand new deflation methodology is also proposed and adopted within the generalized algorithm to find other sensitive eigenvalues. Simulation results illustrate that the generalized algorithm is in a position to not solely maintain the wonderful properties of TSA-SPA in terms of convergence and robustness, however additionally contemplate numerous parameter changes effectively in giant-scale power systems.

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