A Particle Swarm Optimization Based Energy Management Strategy for Hybrid Generation System

Cheng, Yu-Shan; Liu, Yihua; Wang, Shun‐Chung; Peng, Bo-Rei

doi:10.1109/acit-csii-bcd.2017.75

Cited by 2 publications

(1 citation statement)

References 9 publications

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“…To achieve optimum energy flow between sources and load, a genetic algorithm was utilized to fine-tune the parameters of a fuzzy inference system. In [20], particle swarm optimization was used to regulate the energy output of a hybrid power production system. Particle swarm optimization was utilized to maximize the efficiency of the hybrid system's energy management and decrease operational costs by optimizing the production and energy from renewable sources like PV and wind.…”

Section: Introductionmentioning

confidence: 99%

Hybrid DC–AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network

Murugan

Jaishankar

Premkumar³

2022

Energies

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In this research, we introduce an artificial gorilla troop optimizer for use in artificial neural networks that manage energy consumption in DC–AC hybrid distribution networks. It is being proposed to implement an energy management system that takes into account distributed generation, load demand, and battery-charge level. Using the profile data, an artificial neural network was trained on the charging and discharging characteristics of an energy storage system under a variety of distribution-network power situations. As an added bonus, the percentage of mistakes was maintained far below 10%. An artificial neural network is used in the proposed energy management system, and it has been taught to operate in the best possible manner by using an optimizer inspired by gorillas called artificial gorilla troops. The artificial gorilla troops optimizer optimize the weights and bias of the neural network based on the power of the distributed generator, the power of the grid, and the reference direct axis current to obtain most suitable energy management system. In order to simulate and evaluate the proposed energy management system, small-scale hybrid DC/AC microgrids have been created and tested. When compared to other systems in the literature, the artificial gorilla troops optimizer enhanced neural network energy management system has been shown to deliver 99.55% efficiency, making it the clear winner.

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Section: Introductionmentioning

confidence: 99%

Hybrid DC–AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network

Murugan

Jaishankar

Premkumar³

2022

Energies

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Hybrid DC-AC microgrid energy management system using grey wolf optimized neural network

Elsi

Raj²,

Mary

2023

IFS

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Grey wolf-optimized artificial neural networks used in DC–AC hybrid distribution networks, to regulate the energy consumption, is presented in this study. Energy management system that takes into consideration, the distributed generation, load demand, and battery state of charge are being considered. The artificial neural network have been trained, utilising the profile data, based on the energy storage system’s charging and discharging characteristics, under various distribution network power conditions. Moreover, the error rate was kept, well under 10%. The suggested energy management system, that employs an artificial neural network, has been trained to function in the optimal mode, utilising grey wolf optimization for each grid-connected power converter. Small-scale hybrid DC/AC microgrids have been developed and tested, in order to simulate and verify the proposed energy management system. The grey wolf optimized neural network energy management system has been proven to provide 99.48 % efficiency, which is superior when compared to other methods existing in the literatures.

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A Particle Swarm Optimization Based Energy Management Strategy for Hybrid Generation System

Cited by 2 publications

References 9 publications

Hybrid DC–AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network

Hybrid DC–AC Microgrid Energy Management System Using an Artificial Gorilla Troops Optimizer Optimized Neural Network

Hybrid DC-AC microgrid energy management system using grey wolf optimized neural network

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