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All Journal International Journal of Robotics and Control Systems Control Systems and Optimization Letters
Eker, Erdal
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Aerospace Engineering Automotive Engineering Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

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Enhanced RSA Optimized TID Controller for Frequency Stabilization in a Two-Area Power System Ekinci, Serdar; Eker, Erdal; Izci, Davut; Smerat, Aseel; Abualigah, Laith
International Journal of Robotics and Control Systems Vol 4, No 4 (2024)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v4i4.1644

Abstract

This study presents an enhanced reptile search algorithm (ImRSA) optimized tilt-integral-derivative (TID) controller for load frequency control (LFC) in a two-area power system consisting of photovoltaic (PV) and thermal power units. The ImRSA integrates Lévy flight and logarithmic spiral search mechanisms to improve the balance between exploration and exploitation, resulting in more efficient optimization performance. The proposed controller is tested against the original reptile search algorithm (RSA) and other state-of-the-art optimization methods, such as modified grey wolf optimization with cuckoo search, black widow optimization, and gorilla troops optimization. Simulation results show that the ImRSA-optimized TID controller outperforms these approaches in terms of undershoot, overshoot, settling time, and the integral of time-weighted absolute error metric. Additionally, the ImRSA demonstrates robustness in managing frequency deviations caused by solar radiation fluctuations in PV systems. The results highlight the superior efficiency and reliability of the proposed method, especially for renewable energy integration in modern power systems.
Nelder-Mead Enhanced Gazelle Optimizer for Solving Complex Optimization Problems Yağız, Beytullah; Atar, Şeyma Nur; Eker, Erdal; Ekinci, Serdar; Izci, Davut
Control Systems and Optimization Letters Vol 3, No 3 (2025)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/csol.v3i3.240

Abstract

This paper presents the improved gazelle optimization algorithm, which is a new approach in the field of metaheuristic optimization algorithms inspired by nature. By hybridizing the classical gazelle optimization algorithm with the Nelder-Mead simplex method, the improved gazelle optimization algorithm was developed. The proposed IGOA algorithm aims to combine GOA's global search capability with NM's local healing power to provide a more balanced and effective optimization of optimization problems. The performance of the algorithm was evaluated by 30 independent runs on the CEC2017 benchmark functions. The statistical results obtained from the analyses of the mean, standard deviation, best and worst values and Wilcoxon signed ranks test show that IGOA exhibits a superior or competitive performance compared to other current optimization algorithms. Furthermore, the boxplot and convergence curves revealed that IGOA exhibited stable convergence behavior and had a low tendency to get stuck at local optimums. Big-O analysis, on the other hand, confirmed that the algorithm can scale efficiently even in high-dimensional problems. The results prove that the IGOA algorithm is a highly competitive, effective and generalizable tool in solving complex optimization problems.
Co-Authors Abualigah, Laith Atar, Şeyma Nur Ekinci, Serdar Izci, Davut Smerat, Aseel Yağız, Beytullah