scholarly journals Application of the Genetic Algorithms for Identifying the Electrical Parameters of PV Solar Generators

10.5772/22714 ◽  
2011 ◽  
Author(s):  
Anis Sellami ◽  
Mongi Bouaich
Keyword(s):  
Genetic Algorithms ◽  
Electrical Parameters

Parameter Optimization of Photovoltaic Solar Cell and Panel Using Genetic Algorithms Strategy

2016 ◽  
pp. 581-600
Author(s):  
Benmessaoud Mohammed Tarik ◽  
Fatima Zohra Zerhouni ◽  
Amine Boudghene Stambouli ◽  
Mustapha Tioursi ◽  
Aouad M'harer
Keyword(s):  
Genetic Algorithms ◽  
Solar Cells ◽  
Solar Cell ◽  
Numerical Technique ◽  
Simulated Data ◽  
Parameter Extraction ◽  
Electrical Parameters ◽  
Study Results ◽  
The One ◽  
Photovoltaic Solar Cells

In this chapter, we propose to perform a numerical technique based on genetic algorithms (GAs) to identify the electrical parameters (Is, Iph, Rs, Rsh, and n) of photovoltaic (PV) solar cells and modules. The one diode type approach is used to model the I–V characteristic of the solar cell. To extract electrical parameters, the approach is formulated as optimization problem. The GAs approach was used as a numerical technique in order to overcome problems involved in the local minima in the case optimization criteria. Compared to other methods, we find that the GAs is a very efficient technique to estimate the electrical parameters of photovoltaic solar cells and modules. Compared with other parameter extraction techniques, based on statistical study, results indicate the consistency and uniformity of method in terms of the quality of final solutions. In parallel, the simulated data with the extracted parameters of method base with GAs are in very good agreement with the experimental data in all cases.

Parameter Optimization of Photovoltaic Solar Cell and Panel Using Genetic Algorithms Strategy

2016 ◽  
pp. 1371-1390
Author(s):  
Benmessaoud Mohammed Tarik ◽  
Fatima Zohra Zerhouni ◽  
Amine Boudghene Stambouli ◽  
Mustapha Tioursi ◽  
Aouad M'harer
Keyword(s):  
Genetic Algorithms ◽  
Solar Cells ◽  
Solar Cell ◽  
Numerical Technique ◽  
Simulated Data ◽  
Parameter Extraction ◽  
Electrical Parameters ◽  
Study Results ◽  
The One ◽  
Photovoltaic Solar Cells

In this chapter, we propose to perform a numerical technique based on genetic algorithms (GAs) to identify the electrical parameters (Is, Iph, Rs, Rsh, and n) of photovoltaic (PV) solar cells and modules. The one diode type approach is used to model the I–V characteristic of the solar cell. To extract electrical parameters, the approach is formulated as optimization problem. The GAs approach was used as a numerical technique in order to overcome problems involved in the local minima in the case optimization criteria. Compared to other methods, we find that the GAs is a very efficient technique to estimate the electrical parameters of photovoltaic solar cells and modules. Compared with other parameter extraction techniques, based on statistical study, results indicate the consistency and uniformity of method in terms of the quality of final solutions. In parallel, the simulated data with the extracted parameters of method base with GAs are in very good agreement with the experimental data in all cases.

Genetic Algorithms—a Tool for OR?

1996 ◽  
Vol 47 (4) ◽  
pp. 550-561 ◽  
Author(s):  
Kathryn A Dowsland
Keyword(s):  
Genetic Algorithms

Enhanced Artificial Intelligent Method for Representing the Photonic Crystal Fiber Profile Index

2018 ◽  
Vol 1 (1) ◽  
pp. 2-19
Author(s):  
Mahmood Sh. Majeed ◽  
Raid W. Daoud
Keyword(s):  
Genetic Algorithms ◽  
Photonic Crystal ◽  
Photonic Crystal Fiber ◽  
New Method ◽  
Proper Solution ◽  
Artificial Intelligent ◽  
Crystal Fiber ◽  
Fitness Value ◽  
First Pass ◽  
Crossover And Mutation

A new method proposed in this paper to compute the fitness in Genetic Algorithms (GAs). In this new method the number of regions, which assigned for the population, divides the time. The fitness computation here differ from the previous methods, by compute it for each portion of the population as first pass, then the second pass begin to compute the fitness for population that lye in the portion which have bigger fitness value. The crossover and mutation and other GAs operator will do its work only for biggest fitness portion of the population. In this method, we can get a suitable and accurate group of proper solution for indexed profile of the photonic crystal fiber (PCF).

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