Gorilla troops optimization-based load frequency control in PV-thermal power system


Neural Computing and Applications, vol.36, no.8, pp.4179-4193, 2024 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 36 Issue: 8
  • Publication Date: 2024
  • Doi Number: 10.1007/s00521-023-09273-7
  • Journal Name: Neural Computing and Applications
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, Index Islamicus, INSPEC, zbMATH
  • Page Numbers: pp.4179-4193
  • Keywords: Frequency deviation, Gorilla troops optimization, Load frequency control, PV-thermal power system, Tie-line power change
  • Karadeniz Technical University Affiliated: Yes


The mismatch between generated power and load demand often leads to undesirable fluctuations in the frequency and tie-line power change of a power system. To mitigate this problem, the implementation of a control process known as load frequency control (LFC) becomes essential. The objective of this study is to optimize the parameters of the LFC controller for a two-area power system consisting of a reheat thermal generator and a photovoltaic power plant. A proportional–integral (PI) controller is employed to damp the oscillations that occur in the frequency and tie-line power change. A newly developed meta-heuristic optimization technique called gorilla troops optimization (GTO) is used for the first time to optimally tune the parameters of the PI controller and improve its performance. The performance of the GTO optimization technique is analyzed under varying load demands, parameter variations, and nonlinearities. Comparative evaluations with different optimization algorithms are performed. The obtained results demonstrate that the proposed GTO-PI controller outperforms the other optimization techniques in terms of reducing the overshoot values in the system frequency and tie-line power change, as well as achieving faster settling times for these oscillations. This research highlights the effectiveness of the GTO-PI controller in LFC, providing improved performance over alternative algorithms. The results underscore the significance of utilizing meta-heuristic optimization techniques for optimal parameter tuning in power system control applications.