Uncertainty Analysis of Power System Components Based on Stochastic Response Surfaces

The output of a power system analysis mostly requires extensive knowledge and correct handling of input uncertainties. Analytical approaches often depend on simplified models whereas Monte Carlo based sampling methods are often time consuming. This paper presents an uncertainty analysis based on a limited number of well chosen samples which are used to model a stochastic response surface, based on a polynomial of independent standard normal random variables. To illustrate the approach a torque estimation model for induction machines is studied. The model requires different machine parameters as input. The analysis consists of three steps: the characterization of the input uncertainty, the uncertainty propagation and the characterization of the output uncertainty. First a simple sensitivity testing method is performed after which different sampling based methods are compared. The specific advantages of the stochastic response surface method over the Monte Carlo and Latin Hypercube sampling method are shown.