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    AC/DC power systems planning comprising voltage source converters using an enhanced symbiotic organisms search algorithm
    (Springer Science and Business Media Deutschland GmbH, 2025) Battal, Onur; Güvenç, Uǧur
    The concept of distributed energy, where different energy sources are combined in remote locations, forms the basis of today's power systems overall energy production logic. Furthermore, advancements in power electronic infrastructures have emphasized their increased utilization within power systems. In particular, the transition from current source converters (CSC) technology to voltage source converters (VSC) technology has made it easier to integrate power grids with different characteristics into existing power systems. High voltage direct current (HVDC) transmission applications also play a significant role in this integration. In these increasingly complex power systems with various infrastructures and applications, maintaining a sustainable, secure, economical, and environmentally-friendly balance between supply and demand becomes more challenging using classical approaches. In this study, a metaheuristic algorithm is proposed for solving the power flow problems in hybrid AC/DC power systems that include VSC-based, Multi-Terminal HVDC grids. The proposed algorithm is an enhanced version of the symbiotic organisms search (SOS) algorithm and is named di-SOS (diversity improved SOS with Parazite RFDB) algorithm. To demonstrate the effectiveness of the developed algorithm, comparisons were made with SOS algorithm variants and 15 different metaheuristic algorithms found in the literature using various test functions. Nonparametric Wilcoxon signed-rank tests and Friedman tests were performed the compared algorithms and in the comparison between SOS algorithm variants, the di_1-SOS variant of the di_SOS algorithm performed the best with an algorithm score of 2.245. In the comparison with the other 15 metaheuristic algorithms, the di_1-SOS algorithm ranked first with a ranking score of 4.525, demonstrating its success in solving classical test functions. Finally, the algorithm was employed to address power flow problems concerns within hybrid AC/DC power systems, employing altered instances of the IEEE 14-bus and IEEE 30-bus test networks. The acquired outcomes substantiated the efficacy of the algorithm in strategic formulation of AC/DC power systems and in resolving intricate real-world engineering problems, characterized by nonlinearities and constraints. © 2025 Elsevier B.V., All rights reserved.
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    A novel hyper-heuristic algorithm: an application to automatic voltage regulator
    (Springer Science and Business Media Deutschland GmbH, 2024) Hinislioglu, Yunus; Güvenç, Uǧur
    This paper presents a novel optimization algorithm called hyper-heuristic fitness-distance balance success-history-based adaptive differential evolution (HH-FDB-SHADE). The hyper-heuristic algorithms have two main structures: a hyper-selection framework and a low-level heuristic (LLH) pool. In the proposed algorithm, the FDB method is preferred as a high-level selection framework to evaluate the LLH pool algorithms. In addition, a total of 10 different strategies is derived from five mutation operators and two crossover methods for using them as the LLH pool. Balancing the exploration and exploitation capability of FDB is the main reason for being the selection framework of the proposed algorithm. The success of the HH-FDB-SHADE algorithm was tested on CEC-17 and CEC-20 benchmark test suits for different dimensional search spaces, and the obtained solutions from the HH-FDB-SHADE were compared to 10 different LLH pool algorithms. In addition, the HH-FDB-SHADE algorithm has been applied to optimize the control parameters of PID, PIDF, FOPID, and PIDD2 in the optimal automatic voltage regulator (AVR) design problem to reveal the improved algorithm's performance more clearly and prove its success in solving engineering problems. The results obtained from the AVR system are compared with five other effective meta-heuristic search algorithms such as the fitness-distance balance Lévy Flight distribution, differential evolution, Harris–Hawks optimization, Barnacles mating optimizer, and Moth–Flame optimization algorithms in the literature. The results of the statistical analyses indicate that HH-FDB-SHADE is the best-ranked algorithm for solving CEC-17 and CEC-20 benchmark problems and gives better results compared to the LLH pool algorithms. Besides, the proposed algorithm is more effective and robust than five other meta-heuristic algorithms in solving optimal AVR design problems. © 2024 Elsevier B.V., All rights reserved.