NEURAL COMPUTING AND APPLICATIONS, ss.1-15, 2023 (SCI-Expanded)
The mismatch between generated power and load demand often leads to undesirable fluctuations in the frequency and tieline 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 tieline 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.