Abstract
Feature selection of input features is the key issue for pattern recognition-based transient stability assessment (TSA) methods. Considering the possible real-time information provided by phasor measurement units, a group of system-level classification features are firstly extracted from the power system operation condition to construct the original feature set. Then kernelized fuzzy rough sets (KFRS) are used to select the near-optimal feature subset, and Gaussian process is finally employed to test the classification ability of the selected features. The effectiveness of the proposed method is validated by the simulation results on the New England 39-bus test system.
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