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
^RR
IWSS
^TWSVM
is in IWSS
^2FWBs
, and
^RR
IWSSr
^SVM
and
^RR
IWSSr
^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.