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Optimizing Power Distribution Through Electric Load Modelling Using Hybrid Particle Swarm Optimization ? Artificial Neural Network Techniques
Subject area: Science,Engineering and Technology · Area of research: Power systems and Machines
Abstract
The rapid growth in energy demand has highlighted the need for efficient power distribution systems to reduce losses and enhance stability. This thesis presents an innovative approach to optimizing power distribution through electric load modelling by integrating hybrid Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) techniques. Traditional load forecasting and distribution methods often struggle with dynamic and non-linear energy demands, leading to inefficiencies and increased operational costs. The proposed methodology leverages the predictive capabilities of ANN to model complex load behavior accurately while utilizing PSO to optimize the allocation of power resources. By combining these techniques, the system dynamically adjusts power distribution, addressing fluctuations in load demand in real-time. The hybrid algorithm improves convergence speed and enhances the precision of load forecasts, resulting in reduced energy losses and improved grid reliability. Extensive simulations on benchmark power systems demonstrate that the proposed model outperforms conventional techniques in terms of accuracy, efficiency, and adaptability. The research also includes a sensitivity analysis to evaluate the model's robustness under varying load conditions. These findings underscore the potential of integrating advanced computational intelligence methods for achieving sustainable and efficient energy management in modern power systems. This work contributes to the field of smart grid technologies, offering a scalable and adaptive framework for optimizing power distribution in diverse operational scenarios.
Keywords
Alternating Current (AC), Artificial Neural Network (ANN), Direct Current (DC), IEEE 9 ? Bus, Particle Swam Optimization (PSO)
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How to cite this paper
@article{1706730,
author = {Enang Moses, Eronu Emmanuel , Mahmoud Abdullahi, Ejimofor Chijioke},
title = {Optimizing Power Distribution Through Electric Load Modelling Using Hybrid Particle Swarm Optimization ? Artificial Neural Network Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2024},
volume = {8},
number = {6},
pages = {1022-1040},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1706730.pdf},
abstract = {The rapid growth in energy demand has highlighted the need for efficient power distribution systems to reduce losses and enhance stability. This thesis presents an innovative approach to optimizing power distribution through electric load modelling by integrating hybrid Particle Swarm Optimization (PSO) and Artificial Neural Network (ANN) techniques. Traditional load forecasting and distribution methods often struggle with dynamic and non-linear energy demands, leading to inefficiencies and increased operational costs. The proposed methodology leverages the predictive capabilities of ANN to model complex load behavior accurately while utilizing PSO to optimize the allocation of power resources. By combining these techniques, the system dynamically adjusts power distribution, addressing fluctuations in load demand in real-time. The hybrid algorithm improves convergence speed and enhances the precision of load forecasts, resulting in reduced energy losses and improved grid reliability. Extensive simulations on benchmark power systems demonstrate that the proposed model outperforms conventional techniques in terms of accuracy, efficiency, and adaptability. The research also includes a sensitivity analysis to evaluate the model's robustness under varying load conditions. These findings underscore the potential of integrating advanced computational intelligence methods for achieving sustainable and efficient energy management in modern power systems. This work contributes to the field of smart grid technologies, offering a scalable and adaptive framework for optimizing power distribution in diverse operational scenarios.},
keywords = {Alternating Current (AC), Artificial Neural Network (ANN), Direct Current (DC), IEEE 9 ? Bus, Particle Swam Optimization (PSO)},
month = {December},
}