Prediction of the Transient Stability Boundary Based on Nonparametric Additive Modeling

This paper applies modern statistical nonparametric methodology to the problem of prediction of the transient stability boundary of large-scale power engineering systems. The stability issue is characterized by the critical clearing time (CCT) that is employed to determine whether a precontingency steady-state condition is stable for a given fault in the power system. The multidimensional mapping between the precontingency steady-state conditions and the corresponding CCT is modeled as an additive structure of one-dimensional functions. Nonparametric kernel estimation methods are applied to the assumed additive model yielding the boundary prediction algorithm that is easily interpretable and avoids the curse of dimensionality. The precision of our additive nonlinear modeling is demonstrated in the context of fault prediction of the 470-bus power network. For the specified fault type, we demonstrate a stronger prediction accuracy compared to other large-scale machine learning methods that have been used for the transient stability boundary problem so far.