  A new feature selection method based on kernelized fuzzy rough sets (KFRS)
and the memetic algorithm (MA) is proposed for transient stability assessment
of power systems. Considering the possible real-time information provided by
wide-area measurement systems, a group of system-level classification features
are extracted from the power system operation parameters to build the original
feature set. By defining a KFRS-based generalized classification function as
the separability criterion, the memetic algorithm based on binary differential
evolution (BDE) and Tabu search (TS) is employed to obtain the optimal feature
subsets with the maximized classification capability. The proposed method may
avoid the information loss caused by the feature discretization process of the
rough-set based attribute selection, and comprehensively utilize the advantages
of BDE and TS to improve the solution quality and search efficiency. The
effectiveness of the proposed method is validated by the application results on
the New England 39-bus power system and the southern power system of Hebei
province.
