Lfo Damping Enhancement In Multimachine Networks Using African Vultures Optimization Algorithm

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Date

2022-07

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Department of Electrical and Electronic Engineering

Abstract

Low frequency oscillations (LFO) are induced into linked power system networks because of the unstable twist lines connecting the generators. Engineers have long been thought about LFOs because, if they are not quickly damped out, may make the system unbalanced by lowering the damping torque. In this thesis, the African vulture optimization algorithm (AVOA) is used to create robust power system stabilizers (PSS) for multimachine networks. By employing the AVOA optimization approach, the suggested methods damp LFOs by adjusting important parameters of conventional lead-leg type power system stabilizers (CPSS). The main objective function of this thesis is assumed to be maximising the minimum damping ratio. A two-area four machine network and an IEEE-39 bus network that are both responsive to a 3-ϕ fault are being used to analyse this concept. The findings are compared with those of the backtracking search algorithm (BSA), dragonfly algorithm (DA) and particle swarm optimization for the same networks (PSO). Comparative study reveals that the suggested model is trustworthy and robust since they provide effective system damping performance in comparison to BSA, DA and PSO-optimized approaches.

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submitted by Mohammad Forhad, bearing Matric ID. ET-171018 and Mehedi Hasan Shakil, bearing Matric ID. ET-171021 of session Spring 2021

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