A data stream mining algorithm used in power grid disturbance identification

Abstract

The disturbance identification is a classification of data stream problem, VFDT (Very Fast Decision Tree) which is the classical data stream classification algorithm can only deal with discrete attribute values. However, the power grid data gathered by WAMS platform are mostly continuously present. This algorithm is improved on the VFDT, by introducing the sampling theorem, to make the continuous attribute discretization, and classify the common power grid disturbances. At last, the experiments prove that the new algorithm is feasible.