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All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Eyitayo, Aiyedun Olatilewa
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Computer Science & IT Engineering

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A Fletcher-Reeves conjugate gradient algorithm-based neuromodel for smart grid stability analysis Ojo, Adedayo Olukayode; Eyitayo, Aiyedun Olatilewa; Onibonoje, Moses Oluwafemi; Gbadamosi, Saheed Lekan
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp159-165

Abstract

Interest in smart grid systems is growing around the globe as they are getting increasingly popular for their efficiency and cost reduction at both ends of the energy spectrum. This study, therefore, proposes a neuro model designed and optimized with the Fletcher-Reeves conjugate gradient algorithm for analyzing the stability of smart grids. The performance results achieved with this algorithm was compared with those obtained when the same network was trained with other algorithms. Our results show that the proposed model outperforms existing techniques in terms of accuracy, efficiency, and speed. This study contributes to the development of intelligent solutions for smart grid stability analysis, which can enhance the reliability and sustainability of power systems.
Co-Authors Gbadamosi, Saheed Lekan Ojo, Adedayo Olukayode Onibonoje, Moses Oluwafemi