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A new feature selection method based on an improved maximal relevance andminimal redundancy (mRMR) criterion was proposed for power system transientstability assessment. First, the standard mRMR was improved by introducing aweight coefficient in the evaluation criteria to refine the measurement of thefeatures correlation and redundancy. Then, the possible real-time informationprovided by phasor measurement unit (PMU) considered, a group of system-levelclassification features were extracted from the power system operationparameters to build the original feature set, and the improved mRMR wasemployed to evaluate the classification capability of the original features forfeature selection. A group of nested candidate feature subsets were obtained byusing the incremental search technique, and each candidate feature subset wastested by a support vector machine classifier to find the optimal featuresubset with the highest classification accuracy. The effectiveness of theproposed method was validated by the simulation results on the New England39-bus system and IEEE 50-generator test system.

Feature Selection for Transient Stability Assessment Based on Improved Maximal Relevance and Minimal Redundancy Criterion