Probabilistic framework for risk analysis of power system small-signal stability

In power system planning and operation studies, conventional methodologies are still largely used for small-signal stability analysis under the deterministic framework. Nevertheless, no information concerning the small-signal instability risk can be derived and the obtained results are too conservative since deterministic approaches are unable to properly reflect the existing uncertainties in real power systems. Hence, there is a need to develop new approaches which can handle a wider range of operating conditions and consequently capture all of the possible scenarios that may lead to insecure conditions. The present paper explores relevant issues associated with the conditions for application of the Monte Carlo method (MC) in probabilistic eigenanalysis (PE), namely the selection of: a suitable sampling technique for MC-based PE; and a stopping rule which is sufficient to achieve some specified confidence level. The proposed MC-based PE approach provides probabilistic indexes for small-signal stability assessment and enhancement. Several power systems of different sizes and with different small-signal stability performances are used to investigate the feasibility of the probabilistic indexes as well as to compare MC-based PE with an analytical probabilistic approach based on the two-point estimation method (TPE).