Real-time transient stability assessment (TSA) of power systems based on mining system dynamic response has been widely considered by scholars. In this regard, extracting the most discriminative transient features (MDTFs) to achieve high-performance transient stability prediction (TSP) should be regarded as a fundamental issue in the transient learning strategy. In fact, MDTFs extraction is raised to make a trade-off between paradoxically intertwined indices, namely the accuracy and processing time of TSP. To this end, we offer a bi-mode hybrid feature selection scheme called BMHFSS for extracting MDTFs in high dimensional transient multivariate time series (TMTS). First, we used the TMTS, which are effective features on TSA. Next, the trajectory-based filter-wrapper mode (TFWM) is applied on TMTS to surmount the curse of dimensionality in two phases. In the filter phase, statistical and intrinsic characteristics of the TMTS in the form of agglomerative hierarchical clustering (AHC) are measured, and relevant TMTS (RTMTS) is selected according to obtained weight. In the wrapper phase, the RTMTS is entered into the trihedral kernel-based approach, including both fuzzy imperialist competitive algorithm (FICA) and incremental wrapper subset selection (IWSS) to find the intersected most RTMTS (IMRTMTS). As a complementary step, the filter-wrapper scenario in point-based mode (PFWM) is conducted for selecting MDTFs per time series in IMRTMTS. Finally, the aggregated MDTFs (AMDTFs) are tested to verify their efficacy for TSP based on cross-validation. The results show that the proposed framework has prediction accuracy greater than 98 % and a processing time of 52.94 milliseconds for TSA. INDEX TERMSFuzzy imperialist competitive algorithm (FICA), Most discriminative transient features (MDTFs), Support vector machine (SVM), Transient stability assessment (TSA).

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 (F f W v ) that first block-specific F f W v of IWSS 2FWBs and IWSSr 2FWBs includes relevancy ratio-support vector machine (RR-SVM) and second block-specific F f W v of IWSS 2FWBs and IWSSr 2FWBs accompanied by relevancy ratio-twin support vector machine (RR-TWSVM). Generally, 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-F f 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

The corrective action for power systems over transient space requires the transient stability prediction (TSP) in an accurate and timely manner. To this end, applying data mining techniques to achieve high performance on TSP is an inevitable approach. One of the influential factors on TSP performance is extracting the most relevant features (MRFs) of transient data. In this paper, first, we constructed the transient dataset in the form of reactive power-based two-variate time series (RP2vTS). Next, for exploiting the optimal feature subset, the MRFs of RP2vTS are obtained by the 1-persistence parallel fragmented hybrid feature selection scheme (PFHFS) based on filter and wrapper methods. Finally, to evaluate the efficacy of the proposed framework, experimental comparisons on inter-connected New England and New York systems (NETS-NYPS) were applied using the support vector machine (SVM) classifier. The results showed that the proposed framework by selecting MRFs of RP2vTS offers high-performance capacity on TSP.

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

In transient analysis (TA), the processing time (PT) and prediction accuracy (PA) are the most significant indices be influenced the decision-making of grid operators to conduct timely-accurate corrective actions. In fact, achieving low PT and high PA (high-performance TA) necessitates designing the comprehensive feature selection scheme to select optimal transient point features (OTPFs). Hence, the partial-injective trilateral hybrid (filter-wrapper) scheme called PITHS is introduced in this paper. First, the transient dataset in the form of multivariate time series is constructed by an integrated programming platform. Next, based on PITHS, the first univariate trajectory feature (UTF 1 ) is entered into the nested trilateral filter phase (NTFP) equipped with intertwined triple criteria of information theory for selecting filter-OTPFs of UTF 1 (f-1 OTPFs). Then, f-1 OTPFs are fed to the nested trilateral wrapper phase (NTWP) for selecting filter-wrapper-1 OTPFs (fw-1 OTPFs). The NTWP is including the hyperplane-based predictive approach accompanied by the triple kernel. After conducting NTWP, fw-1 OTPFs are considered as the first ultimate optimal point features ( 1 UOPFs). Next, survived fw-1 OTPFs injected into the subsequent trajectory (UTF 2 ), and the neo-formed trajectory (fw-1 OTPFs plus UTF 2 ) drives a new round of NTFP and NTWP for finding fw-2 OTPFs ( 2 UOPFs). By conducting this procedure on the last neo-formed trajectory (the fw-k-1 OTPFs+UTF k ), the fw-k OTPFs are obtained ( k UOPFs). Finally, the 1:k UOPFs set is tested to verify their efficacy for TA based on the cross-validation technique. The obtained results show that the proposed framework has a prediction accuracy of 98.75 % and a processing time of 152.591 milliseconds for TA.INDEX TERMS Hybrid feature selection scheme, Optimal transient point features (OTPFs), Support vector machine (SVM), Transient stability assessment (TSA).