Abstract
Wind farms are increasingly installed in power systems. Such power generation units increase stochastic power system variables, affecting power systems stability. Therefore, the impact of stochastic generation on power systems stability should be carefully taken into account, an aim which can be realized by probabilistic evaluation. This paper presents hybrid methods for the evaluation of probabilistic small-signal stability (PSSS) in power systems. These methods are based on clustering approaches and Monte Carlo simulation (MCS) which is employed in a probabilistic problem to achieve acceptable results. There are two steps for evaluation of PSSS in the hybrid methods. Clustering methods divide stochastic sets into small sets; the member of small sets is employed to calculate eigenvalues similar to MCS. Consequently, this method is faster than MCS. Two case studies are employed based on the IEEE 9-bus and 39-bus test systems for the evaluation of the proposed method. It is shown that the proposed methods yield accurate results and are faster than MCS.
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