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Deterministic strategies are still largely used for small signal stability (SSS) assessment and enhancement in most power systems worldwide. However, the solutions obtained with such strategies are very limited since they are correct just around the particular conditions analysed. Therefore it is essential to develop comprehensive strategies to cope with more operating conditions and random factors in SSS studies. This paper presents the development and application of a probabilistic methodology for SSS assessment and enhancement. The approach accounts for uncertainties of generation and nodal load demands as well as the effects of system element outages. Probabilistic performance indexes based on a combination of Monte Carlo method and fuzzy clustering are calculated. It is shown how properly statistical processing of output variables of interest can be adapted to evaluate the proposed indexes, which are the instability risk index and two additional indexes concerning power system stabiliser location and transfer capability as affected by SSS. The results obtained using a 18-power plant power system are analysed and compared against the results obtained through a deterministic approach. Relevant discussion highlights the viewpoint and effectiveness of the proposed methodology in providing instability risk assessment and useful information that aims at minimising the occurrence and impacts of electromechanical oscillations in the context of power system operation around uncertain load conditions.