Feature Selection for Transient Stability Assessment Based on Improved Maximal Relevance and Minimal Redundancy Criterion
For power system operators, this method improves the accuracy of transient stability assessment by refining feature selection, but the improvement is incremental.
The paper proposes an improved mRMR feature selection method for power system transient stability assessment, achieving higher classification accuracy on the New England 39-bus and IEEE 50-generator test systems.
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.