Surviving Most Relevant Features on Transient Trajectories Data by Dyadic 24-Way Hybrid Feature Selection Algorithm for Transient Stability Prediction

Abstract

Designing an effective feature selection scheme (FSS) is an inevitable solution for top-level balancing contrastive-correlated indices, namely transient processing time (TPT) and transient prediction accuracy (TPA) on transient stability assessment (TSA). Achieving low TPT and high TPA have a tight relationship in selecting the most relevant transient point features (MRTPFs) survived by applying comprehensive FSS on m-variate transient trajectory features (mVTTFs). Hence, we introduce dyadic 24way hybrid FSS (D24WHFSS) to select MRTPFs from mVTTFs. The D24WHFSS comprises 24 permutations of the chained four-stage hybrid structure called 24-way hybrid FSS (24WHFSS). The 24WHFSS raised by bi-incremental wrapper mechanism (bi-IWM) contains incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr). Each hybrid scenario is equipped with symmetric uncertainty (SU) (filter phase) and dual support vector-based classifiers (DSVCs) (wrapper phase). Embedded DSVCs into IWSS/ IWSSr include kernel support vector machine (kSVM) and k-twin SVM (kTWSVM). By plugging dual kernel function pairs (DKFPs) into DSVCs, 24-way SU bi-IWM DSVCs is exerted in the varied twofold repetition (dyadic 24WHFSS). In the first KFP (KFP 1 ), the radial basis function (RBF) is situated in the DSVCs of bi-IWM. In KFP 2 , the dynamic time warping (DTW) and