Feature selection for transient stability assessment based on kernelized fuzzy rough sets and memetic algorithm

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.