2014
DOI: 10.1016/j.jpowsour.2014.01.121
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Design and control strategy for a hybrid green energy system for mobile telecommunication sites

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Cited by 25 publications
(17 citation statements)
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“…The common methods that are used to optimize the power supply systems include Genetic Algorithm (GA) [66,67,101,105,108,109,116,[127][128][129], Particle Swarm Optimization (PSO) [71,78,128,130,131], Simulated Annealing (SA) [132,133], Honey Bee Mating Optimization (HBMO) [134,135], Neural Networks (NN) [111,114,136,137], Evolutionary Algorithm (EA) [66,116,134,138], and Simplex Algorithm [128,134,139,140]. In the context of remote telecommunication applications, Okundamiya et al [127] applied the GA approach to optimize a hybrid PV-wind-battery system for a BS site.…”
Section: System Sizing and Optimizationmentioning
confidence: 99%
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“…The common methods that are used to optimize the power supply systems include Genetic Algorithm (GA) [66,67,101,105,108,109,116,[127][128][129], Particle Swarm Optimization (PSO) [71,78,128,130,131], Simulated Annealing (SA) [132,133], Honey Bee Mating Optimization (HBMO) [134,135], Neural Networks (NN) [111,114,136,137], Evolutionary Algorithm (EA) [66,116,134,138], and Simplex Algorithm [128,134,139,140]. In the context of remote telecommunication applications, Okundamiya et al [127] applied the GA approach to optimize a hybrid PV-wind-battery system for a BS site.…”
Section: System Sizing and Optimizationmentioning
confidence: 99%
“…In the context of remote telecommunication applications, Okundamiya et al [127] applied the GA approach to optimize a hybrid PV-wind-battery system for a BS site. The GA uses three operators (i.e., selection, crossover, and mutation) and the selection process begins by evaluating the initial system design (known as chromosome, chosen or set by default) to preliminarily determine a reliable power supply to meet the load demand.…”
Section: System Sizing and Optimizationmentioning
confidence: 99%
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