Finding Optimal Point Features in Transient Multivariate Excursions by Horizontally Integrated Trilateral Hybrid Feature Selection Scheme for Transient Analysis

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 (UTF¹) is entered into the nested trilateral filter phase (NTFP) equipped with intertwined triple criteria of information theory for selecting filter-OTPFs of UTF¹ ( $f$ -¹OTPFs). Then, $f$ -¹OTPFs are fed to the nested trilateral wrapper phase (NTWP) for selecting filter-wrapper-¹OTPFs (fw-¹OTPFs). The NTWP is including the hyperplane-based predictive approach accompanied by the triple kernel. After conducting NTWP, fw-¹OTPFs are considered as the first ultimate optimal point features (¹UOPFs). Next, survived fw-¹OTPFs injected into the subsequent trajectory (UTF²), and the neo-formed trajectory (fw-¹OTPFs plus UTF²) drives a new round of NTFP and NTWP for finding fw-²OTPFs (²UOPFs). By conducting this procedure on the last neo-formed trajectory (the fw-^k−1OTPFs + UTF^k), the fw-^kOTPFs are obtained (^kUOPFs). Finally, the ^1:kUOPFs 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.