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A new feature selection method based on kernelized fuzzy rough sets (KFRS)and the memetic algorithm (MA) is proposed for transient stability assessmentof power systems. Considering the possible real-time information provided bywide-area measurement systems, a group of system-level classification featuresare extracted from the power system operation parameters to build the originalfeature set. By defining a KFRS-based generalized classification function asthe separability criterion, the memetic algorithm based on binary differentialevolution (BDE) and Tabu search (TS) is employed to obtain the optimal featuresubsets with the maximized classification capability. The proposed method mayavoid the information loss caused by the feature discretization process of therough-set based attribute selection, and comprehensively utilize the advantagesof BDE and TS to improve the solution quality and search efficiency. Theeffectiveness of the proposed method is validated by the application results onthe New England 39-bus power system and the southern power system of Hebeiprovince.

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