PARAMETRIC VARIATIONS IN DYNAMIC-MODELS OF INDUCTION MACHINE CLUSTERS

This paper presents a probabilistic approach to the characterization o f dynamical models of induction machine clusters. Our method derives b ounds on eigenvalue variations for linearized models expressed in term s of stochastic norms. In our examples of modeling of power system loa ds this characterization tends to be less conservative than alternativ e deterministic approaches. We consider examples of induction machines with different ratings (classes), and allow for wide variations of el ectrical and mechanical parameters. We describe a stochastic norm appr oach to: 1) efficiently describe the dynamical model variations for a cluster of similar machines without having to perform repeated eigenva lue calculations, e.g. in a wind farm application, and 2) suggest the order of the reduced model in power system load modeling where the tig htness of the bounds of eigenvalue variations is used for guidance in decisions regarding the number of different classes that would efficie ntly represent a given composite load.