TY  - JOUR
AU  - Forhad, Mohammad
AU  - Shakil, Mehedi Hasan
AU  - Islam, Md Rashidul
AU  - Shafiullah, Md
AU  - Worku, Muhammed
PY  - 2024
DA  - 2024/01/30
TI  - LFO Damping Enhancement in Multimachine Network Using African Vulture Optimization Algorithm
JO  - Journal of Energy and Power Technology
SP  - 003
VL  - 06
IS  - 01
AB  - The prolonged presence of low-frequency oscillation (LFO) in power system networks (PSN) poses a significant threat to their stability. Hence, engineers and researchers have continuously developed effective strategies to mitigate the issue and enhance the stability of the PSN. This article proposes a new approach using the African Vultures Optimization Algorithm (AVOA) to design robust Power System Stabilizers (PSS) and enhance the LFO damping in multi-machine networks. The damping ratio-based objective function minimizes the oscillations and increases the system damping. Conventional power system stabilizer (CPSS) is adopted as its parameters are tuned with the help of the African Vulture optimization algorithm to achieve a proper damping ratio over a wide range. Using a pair of multi-machine networks likely to experience three-phase faults, we examine the execution of the process. The results obtained by the simulations are compared with the three reputable optimization algorithms called particle swarm optimization (PSO), backtracking search algorithm (BSA), and dragonfly algorithm (DA), and AVOA-tuned PSS outperforms in terms of minimum damping ratio for tested PSN (Network-1 and Network-2). The AVOA provides a percentage improvement of 76%, 50%, 22%, and 25% compared to CPSS, PSO, BSA, and DA, respectively, for Network-1 and 85%, 83%, and 10% for PSO, BSA, and DA, respectively for Network-2. Therefore, the proposed AVOA optimization technique surpasses other methods to enhance the tested networks' minimum damping ratio.
SN  - 2690-1692
UR  - https://doi.org/10.21926/jept.2401003
DO  - 10.21926/jept.2401003
ID  - Forhad2024
ER  -