  A new feature selection method based on an improved maximal relevance and
minimal redundancy (mRMR) criterion was proposed for power system transient
stability assessment. First, the standard mRMR was improved by introducing a
weight coefficient in the evaluation criteria to refine the measurement of the
features correlation and redundancy. Then, the possible real-time information
provided by phasor measurement unit (PMU) considered, a group of system-level
classification features were extracted from the power system operation
parameters to build the original feature set, and the improved mRMR was
employed to evaluate the classification capability of the original features for
feature selection. A group of nested candidate feature subsets were obtained by
using the incremental search technique, and each candidate feature subset was
tested by a support vector machine classifier to find the optimal feature
subset with the highest classification accuracy. The effectiveness of the
proposed method was validated by the simulation results on the New England
39-bus system and IEEE 50-generator test system.
