Otomatik gerilim regülatör sistemi için karşıt tabanlı atom arama optimizasyon algoritması

Serdar Ekinci*, Ayşen Demirören, Hatice Lale Zeynelgil, Baran Hekimoğlu

*Bu çalışma için yazışmadan sorumlu yazar

Araştırma sonucu: ???type-name???Makalebilirkişi

22 Atıf (Scopus)

Özet

This article presents a modified version of atom search optimization (ASO) algorithm that uses the opposition-based learning (OBL) to improve the search space exploration. OBL is a commonly used machine learning strategy for increasing the performance of meta-heuristic algorithms. As a new design method, the opposition-based ASO (OBASO) algorithm was proposed for the first time in determining the optimum values of the proportional-integral-derivative plus second order derivative (PIDD2) controller parameters in an automatic voltage regulator (AVR) system. In the design problem, a new objective function, including the integral of time-weighted squared error (ITSE) and overshoot all together, was optimized with the proposed OBASO algorithm to find the best values of the PIDD2 controller parameters. The performance of the proposed OBASO tuned PIDD2 (OBASO-PIDD2) controller is compared to that of the classic ASO tuned PIDD2 (ASO-PIDD2) controller as well as the PID, fractional order PID (FOPID) and PIDD2 controllers tuned with modern meta-heuristic algorithms. Comparative transient and frequency response analyzes were conducted to assess the stability of the proposed approach. In addition, considering the possible changes in AVR parameters, the robustness of the proposed approach was tested. The extensive simulation results and comparisons with other existing controllers show that the proposed OBASO-PIDD2 controller with a new objective function has a superior control performance and can highly improve the system robustness with respect to model uncertainties.

Tercüme edilen katkı başlığıAn opposition-based atom search optimization algorithm for automatic voltage regulator system
Orijinal dilTürkçe
Sayfa (başlangıç-bitiş)1141-1157
Sayfa sayısı17
DergiJournal of the Faculty of Engineering and Architecture of Gazi University
Hacim35
Basın numarası3
DOI'lar
Yayın durumuYayınlandı - 2020

Bibliyografik not

Publisher Copyright:
© 2020 Gazi Universitesi Muhendislik-Mimarlik. All rights reserved.

Keywords

  • Atom search optimization algorithm
  • Automatic voltage regulator
  • Opposition-based learning
  • Parameter tuning

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