Identification of Nonlinear System using Evolutionary Differential and Grey Wolf Optimizer

Bergamini, Mariane Gavioli; Bonfim, Marlio; Leandro, Gideon Villar; Oliveira, Jhonys Leite de

doi:10.26678/abcm.cobem2017.cob17-2029

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…Omega (ω) is expected for the outstanding probable results. The GWO algorithm's optimization is guided by α and β; these wolves are followed by means of the ω wolves [27]. The next is a short-lived description of the mathematical model.…”

Section: Grey Wolf Optimization Algorithmmentioning

confidence: 99%

Electrical and thermal performance analysis of hybrid photovoltaic/thermal water collector using meta-heuristic optimization

Aggour,

Atia,

Farghally

et al. 2024

Journal of Electrical Systems and Inf Technol

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The photovoltaic/thermal (PV/T) flat-panel technology has numerous advantages over PV modules and separately mounted solar thermal collectors regarding overall effectiveness and space-saving. Hybrid PV/T solar collectors’ thermal and electrical performance is influenced by design parameters like mass flow rate, tube diameter, tube spacing, packing factor, and absorber conductivity. This paper focused on using several meta-heuristic optimization techniques, incorporating the following: multiverse algorithm, dragonfly algorithm, sine–cosine algorithm, moth-flame algorithm, whale algorithm, particle swarm algorithm, ant-lion algorithm, grey wolf algorithm, and particle swarm optimization algorithm in PV/T collector optimal design according to maximum total efficiency obtained. The outcomes of the various algorithms revealed that the maximum electrical efficiency of the PV/T collector ranged from 13.85 to 14.28%, while the maximum thermal efficiencies ranged from 41.41 to 52.08% under standard test conditions (1000 W/m2 and 25 °C). The optimized values for the design parameters of the PV/T collector were as follows: the absorber conductivity was determined to be 356.6 W/m K, the packing factor was optimized to 0.7, the mass flow rate was set at 0.019 kg/s, the tube width was determined to be 0.035 m, and the tube spacing was optimized to 0.0524 m. The results indicated that the grey wolf optimizer (GWO) algorithm proved to be highly effective in optimizing the design parameters of PV/T collectors. Furthermore, the study examined the relationship between the temperature of PV modules and PV/T collectors by considering variations in mass flow rate, packing factor, and tube width at different solar radiation levels. The results confirmed that the PV/T collector temperature exhibited improvements compared to the PV module temperature. As a result, this led to higher electrical efficiency and an overall increase in the total efficiency of the PV/T collector.

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Section: Grey Wolf Optimization Algorithmmentioning

confidence: 99%

Electrical and thermal performance analysis of hybrid photovoltaic/thermal water collector using meta-heuristic optimization

Aggour,

Atia,

Farghally

et al. 2024

Journal of Electrical Systems and Inf Technol

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Parameter estimation of the hydraulic turbine regulator system using multi-objective lion algorithm

Bergamini

Oliveira

Ribeiro

et al. 2023

IFS

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Accurate modeling of electric power generating unit and its hydraulic turbine regulation systems provides support for the speed controller synthesis and stability analysis. It is however a difficult task due to the presence of many non-linear factors in this system. an approach to estimate the parameters of hydraulic turbine regulatory system models is to derive the physical representation of each component and, through simulation, to compare to compare their models, outputs with real data obtained from a hydroelectric plant located in Brazil. The objective of this paper is to find the best values that will represent the system under study as a whole. This problem can be seen as an optimization problem. To find its feasible and optimal solution, this work proposes a new metaheuristics multi-objective based on the Lion Algorithm (LA), called the Multi-Objective Lion Algorithm (MOLA), and its application in the estimation of parameters of the system under study. In addition, the new metaheuristic proposed is validated by using a set of benchmark cases. The results have demonstrated that MOLA outperforms or at least performs similarly to Multi-objective Grey Wolf Optimizer (MOGWO), Multiple Objective Particle Swarm Optimization (MOPSO), Multi-objective Salp Swarm Algorithm (MSSA), Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D), and Non-dominated Sorting Genetic Algorithm III (NSGA-III) in the optimization of multi-objective benchmark functions. These results, suggest that the proposed MOLA algorithm works efficiently.

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Identification of Nonlinear System using Evolutionary Differential and Grey Wolf Optimizer

Cited by 2 publications

References 7 publications

Electrical and thermal performance analysis of hybrid photovoltaic/thermal water collector using meta-heuristic optimization

Electrical and thermal performance analysis of hybrid photovoltaic/thermal water collector using meta-heuristic optimization

Parameter estimation of the hydraulic turbine regulator system using multi-objective lion algorithm

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