An improved constant current step-based grey wolf optimization algorithm for photovoltaic systems

Dagal I., AKIN B., Dari Y. D.

Journal of Intelligent and Fuzzy Systems, vol.46, no.4, pp.8441-8460, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 46 Issue: 4
  • Publication Date: 2024
  • Doi Number: 10.3233/jifs-224535
  • Journal Name: Journal of Intelligent and Fuzzy Systems
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Aerospace Database, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, Computer & Applied Sciences, INSPEC, Metadex, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.8441-8460
  • Keywords: Grey wolf optimization, maximum power point, metaheuristics, photovoltaics
  • Yıldız Technical University Affiliated: Yes


In this paper, an improved constant current step based on the grey wolf optimization (CCS-GWO) algorithm for photovoltaic systems is investigated. The development of grey wolf optimization has been widely spread over photovoltaic applications. This method is one of the metaheuristic swarm optimization algorithm groups inspired by an optimum means of chasing prey by grey wolves. The proposed technique applies constant current steps to the pack of wolves (alpha, beta, and omega) by monitoring the average of the internal current step and external current step in order to target the leader alpha wolf position. Moreover, the proposed technique solves the convergence process issues, low convergence speed, and premature local optima problems of the traditional GWO algorithm. This CCS-GWO algorithm accurately tracks the maximum power point from the photovoltaic systems for load charging in different partial shading conditions (PSCs). A number of standard benchmark functions are presented with low average cost functions and their corresponding standard deviation values. The simulation results revealed that the proposed CCS-GWO approach outperforms the existing GWO and GA algorithms in terms of efficiency (98.55%) and tracking time (0.3 s).