Controller design for automatic voltage regulator system using modified opposition-based weighted mean of vectors algorithm

Abstract

ABSTRACTThis paper proposes a modified optimization technique to determine the parameters of a real proportional-integral-derivative plus second-order derivative (real PIDD2) controller adopted in an automatic voltage regulator (AVR). In this regard, a modified opposition learning (mOBL) based weighted mean of vectors (INFO) algorithm (mOBL-INFO) is proposed for the first time. The performance of the proposed algorithm was initially tested on several benchmark functions with unimodal, multimodal, and low-dimensional properties. The obtained results against test functions were compared with the original INFO algorithm as the latter has already been shown to present superior results against several recent and effective metaheuristic algorithms. The developed mOBL-INFO algorithm was then used to adjust a real PIDD2 controller for the AVR system. The performance of the proposed method was tested using several analyses such as transient response, stability, and robustness. The related analyses demonstrated good promise of the proposed method for AVR system control. Furthermore, previously reported 26 effective methods were also employed to assess the performance of the mOBL-INFO-based real PIDD2 controller for the AVR system. The obtained results demonstrated that the proposed method in this study has an excellent transient response performance for AVR system control.KEYWORDS: Automatic voltage regulatorreal PID plus second-order derivative controllerweighted mean of vectors algorithmopposition-based learning mechanismMetaheuristics Disclosure statementThe authors have no affiliation with any organization with a direct or indirect financial interest in the subject matter discussed in the manuscript. This manuscript has not been submitted to, nor is under review at, another journal or other publishing venue.Data availability statementData sharing is not applicable to this article as no datasets were generated or analyzed during the current study.Compliance with ethical standardsThis article does not contain any studies with human participants and/or animals performed by any authors.Additional informationFundingNo funding has been received for this work.Notes on contributorsSerdar EkinciSerdar Ekinci received his BSc degree in Control Engineering, and his MSc and PhD degrees in Electrical Engineering all from Istanbul Technical University (ITU), in 2007, 2010 and 2015, respectively. He is currently an Associate Professor working in Department of Computer Engineering at Batman University, Turkey. His areas of interest are electrical power systems, stability, control technology and the applications of metaheuristic optimization algorithms to various control systems.Özay CanÖzay Can received his BSc, MSc, and PhD degrees in Electrical Electronics Engineering all from Duzce University, in 2011, 2016, and 2022, respectively. He is currently an Assistant Professor working in Department of Electronics and Automation at Recep Tayyip Erdogan University, Turkey. His areas of interest are power systems, control systems applications of metaheuristic optimization algorithms.Davut IzciDavut Izci received his BSc degree from Dicle University, Turkey, in Electrical and Electronic Engineering and his MSc and PhD degrees from Newcastle University, England – UK, in Mechatronics and Microsystems, respectively. He is currently an Associate Professor working on optimization, control system design, sensing applications, energy harvesting, microsystems development and applications of metaheuristic optimization techniques to different control systems and real-world engineering problems.