Extracting Most Discriminative Features on Transient Multivariate Time Series by Bi-Mode Hybrid Feature Selection Scheme for Transient Stability Prediction

Abstract

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).

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

“…ReliefF [15] and FCBF [16]. Also, D24WHFSS is compared with 3MCWHFSSs including BMHFSS [19], CPQHFSS [22], and PITHS [23]. The 28VTTFs are fed to the 3MWHFSSs and 3MCWHFSSs, the 3MWHFSSs-based OFs and 3MCWHFSSs-based OFs are selected.…”

“…As can be seen in Table 12, D24WHFSS-based UMRTPFs 1:28 have better performance in TSP than 3MWHFSSs OFs and 3MCWHFSSs OFs . According to Table 12, the obtained results manifested the D24WHFSS by selecting 88-cycles of 28VTTFs (See Table 8, last row), which has better performance (Acc, TPR, and TNR) than mRMR OFs (9 cycles-4VTTFs), FCBF OFs , ReliefF OFs , and BMHFSS OFs (9 cycles-3VTTFs) [19], CPQHFSS OFs (48 cycles-28VTTFs [22]), and PITHS OFs (24 cycles-18VTTFs [23]). Based on the TPT index, the obtained results (See Table 13) show that the coupling D24WHFSS OFs and SVM RBF has a higher TPT (102.607 ms) than SVM RBF -3MWHFSSs OFs (SVM RBF -mRMR OFs : 68.793 ms, SVM RBF -FCBF OFs : 68.930 ms, SVM RBF -ReliefF OFs : 68.910 ms) and SVM RBF -BMHFSS OFs with 52.948 ms. Also, SVM RBF -D24WHFSS OFs has lower TPT than SVM RBF -PITHS OFs with 152.591 ms and SVM RBF -CPQHFSS OFs with 152.525 ms.…”

“…In [18], hybrid FSS is appeared by integrating the normalized mutual information (NMI) (filter phase) and binary particle swarm optimization (BPSO) (wrapper phase) for highperformance transient stability status prediction. To surmount the HDTS, Reference [19] presents the point and trajectory-feeding hybrid algorithm in the form of coupling fuzzy imperialist competitive algorithm (FICA) and incremental wrapper subset selection (IWSS) called FICA-IWSS that includes MI and conditional MI metrics in the filter phase and kernel SVM in the wrapper phase. In [20], a BinJava-based kernelized fuzzy rough sets (KFRS) approach is conducted on the entire feature space for selecting optimal feature subsets.…”

“…Focusing on the past FS-based TSA studies (e.g., [13]- [19], [22], [23]) revealed the released strategies suffer from the mono-way filter-wrapper structure (MWFWS) that causes failure in the precise exploring MRTPFs from nonlinear HDTS. Passing the weak-learner MWFWS gates requires designing the well-structured FSS supported by a multi-level circular learning model (MLCLM).…”

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

“…Based on the proposed hybrid FSS in [22], first, normalized mutual information (NMI) ranks the initial features in the form of strongly relevant feature subset (SRFS) and the weakly relevant feature subset (WRFS). Next, the obtained knowledge of the filter phase is fed to the wrapper phase equipped with an easy-implementing search algorithm called binary particle swarm optimization (BPSO) to improve the effectiveness of FSS results.Considering high-dimensional multivariate time series data obtained by transient simulations, Reference [23] designed hybrid FSS in bi-mode, including trajectory-based filter-wrapper method (TFWM) and pointbased filter-wrapper method (PFWM). In TFWM, mutual information-entropy-based (MIE) calculations (filter) and fuzzy imperialist competitive algorithm (FICA)-IWSS-based trihedral kernel-SVM (wrapper) find the optimal transient series.…”