Comparison of point estimate and cumulant techniques for efficient estimation of critical oscillatory modes

This paper investigates the use of point estimate and cumulant-based techniques for the efficient estimation of critical eigenvalue distributions during probabilistic small-disturbance stability studies. The presence of uncertainty in system operating conditions results in variation of the damping of critical electromechanical modes, affecting system stability. The efficient estimation techniques are evaluated to assess their suitability for fast estimation of variation in critical electromechanical modes and robustness to variation in system uncertainties. The accuracy and computational efficiency of the methods is compared with the numerical Monte Carlo approach.