Widi Aribowo, Heri Suryoatmojo, Feby Agung Pamuji
The hybrid approach presented in this study combines the Osprey Optimization Algorithm (OOA) with Lévy flight optimization. It is possible to mimic an osprey’s natural behavior by using the Osprey Optimization Algorithm (OOA) approach. This hunting approach is used by ospreys to locate their prey, hunt it, and then position it so that it may be consumed. The stride length during a Lévy’s flight, a specific kind of random walk, can be described by a heavy-tailed probability distribution. OOA is the primary algorithm of the suggested approach, which is coupled with the Lévy flight optimization technique. We refer to this method as MOOA. The suggested approach is used for droop control’s Proportional-Integral (PI) secondary control optimization. This article compares the Aquila Optimizer (AO), Whale Optimization Algorithm (WOA), Marine Predator Algorithm (MPA), Golden Jackal Optimization Algorithm (GJO), and Reptile Search Algorithm (RSA) to assess the efficacy of the suggested approach. Performance tests on droop control are compared with convergence curves in this test. The simulation results show that the proposed method performs strongly and promisingly when applied to secondary control in droop control. © ICIC International 2024.
Department of Electrical Engineering, Institut Teknologi Sepuluh, Nopember B, C and AJ Building, Kampus ITS, Sukolilo, Surabaya, 60111, Indonesia; Department of Electrical Engineering, Faculty of Vocational, Universitas Negeri Surabaya, K4 Building, Jl. Kampus, Ketintang, Surabaya, 60213, Indonesia
