Application of quasi-oppositional driving training-based optimization for a feasible optimal power flow solution of renewable power systems with a unified power flow controller

Frontiers in Energy Research, Год журнала: 2025, Номер 13

Опубликована: Май 29, 2025

The current study’s objective is to reveal the best possible solution for an optimal power flow (OPF) problem. driving training-based optimization (DTBO) technique has been applied in this work achieve goal where quasi-oppositional based learning (QOBL) integrated with DTBO and referred as (QODTBO). experiments have carried out on IEEE 57 & 118 bus systems. Four different test scenarios considered here. first one traditional network; renewable energy sources (RESs) (i.e., solar wind units) chosen second one, third considers RESs unified controller (UPFC) finally network UPFC. In each scenario, there are four functions, among which single three of them multi-objective. Obtaining minimum total cost comes under single-objective function. Simultaneous reduction overall emission, concurrent voltage deviation (VD), simultaneous stability index come multi-objective cases. acquired outcomes by QODTBO contrasted found use DTBO, backtracking search algorithm (BSA), sine cosine (SCA). effect inherent uncertainties within gauged study choice appropriate probability density functions (PDF). Based experimental using techniques over thirty trials, a statistical report prepared that ascertains most robust scheme tools taken into consideration study. To represent analysis, pictorially box plots error-bar provided. One-way analysis variance (ANOVA) tests also conducted enhance degree reliability inferences made results. From work, it explored integrating UPFC IEEE-57 system can improve execution system. If performances conventional system, RES-based RES- UPFC-based observed, be noticed reduction, gives better result 1.364790635% 2.175247484% compared

Язык: Английский

Adaptive dynamic prediction mechanism and heuristic algorithm based fast threshold selection for reversible data hiding

Expert Systems with Applications, Год журнала: 2025, Номер 288, С. 128251 - 128251

Опубликована: Май 29, 2025

Язык: Английский