Applying Cross-Permutation-Based Quad-Hybrid Feature Selection Algorithm on Transient Univariates to Select Optimal Features for Transient Analysis

Neglect feature selection matter for high-dimensional transient data obtained from phasor measurement units (PMUs) negatively affect the inconsistent-linked indices, namely data labeling time (DLT) and data labeling accuracy (DLA) in the transient analysis (TA). A reasonable trade-off between DLT and DLA or a win-win solution (low DLT and high DLA) necessitates feature-based mining on transient multivariate excursions (TMEs) via designing the comprehensive feature selection scheme (FSS). Hence, to achieve high-performance TA, we offer the cross-permutation-based quad-hybrid FSS (CPQHFSS) to select optimal features from TMEs. The CPQHFSS consists of four filter-wrapper blocks (FWBs) in the form of twin two-FWBs mounted on two-mechanism of the incremental wrapper, namely incremental wrapper subset selection (IWSS) and IWSS with replacement (IWSSr). The IWSS^2FWBs and IWSSr^2FWBs contain filter-fixed and wrapper-varied approaches ( $\text{F}^{f}\text{W}^{\mathrm {v}}$ ) that first block-specific $\text{F}^{f}\text{W}^{\mathrm {v}}$ of IWSS^2FWBs and IWSSr^2FWBs includes relevancy ratio-support vector machine (RR-SVM) and second block-specific $\text{F}^{f}\text{W}^{\mathrm {v}}$ of IWSS^2FWBs and IWSSr^2FWBs accompanied by relevancy ratio-twin support vector machine (RR-TWSVM). Generally, $^{\mathrm { \textrm {}RR}}$ IWSS^SVM and ^RRIWSS^TWSVM is in IWSS^2FWBs, and ^RRIWSSr^SVM and ^RRIWSSr^TWSVM is in IWSSr^2FWBs. Besides direct relations in two- $\text{F}^{f}\text{W}^{v}$ Bs per incremental wrapper mechanism, by plugging different kernels into the hyperplane-based wrapper, all possible cross-permutations of hybrid FSS are applied on transient data to extract the optimal transient features (OTFs). Finally, the evaluation of the effectiveness of the CPQHFSS-based OTFs in TA is conducted based on the cross-validation technique. The obtained results show that the proposed framework has a DLA of 98.87 % and a DLT of 152.525 milliseconds for TA.