Simulated Annealing algorithm for photovoltaic parameters identification

El-Naggar, K. M.; AlRashidi, M.R.; AlHajri, M. F.; Al-Othman, A. K.

doi:10.1016/j.solener.2011.09.032

Cited by 494 publications

(207 citation statements)

References 15 publications

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“…This experimentally measured I-V curve corresponds to a Photowatt-PWP 201 solar module (Photowatt, Bourgoin-Jallieu, France), composed of 36 solar cells connected in series and measured at 45 ° C. It has been widely used by different authors as benchmark to check electric circuit models and methods developed for parameter extraction [30][31][32][33][48][49][50][51]. The three characteristic points of the I-V curve are included in Table 9, these points were extrapolated in Ref.…”

Section: Tablementioning

confidence: 99%

On the analytical approach for modeling photovoltaic systems behavior

Cubas

Pindado

Victoria

2014

Journal of Power Sources

216

176

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The l-diode/2-resistors electric circuit equivalent to a photovoltaic system is analyzed. The equations at particular points of the l-V curve are studied considering the maximum number of terms. The maximum power point as a boundary condition is given special attention. A new analytical method is developed based on a reduced amount of information, consisting in the normal manufacturer data. Results indicate that this new method is faster than numerical methods and has similar (or better) accuracy than other existing methods, numerical or analytical.

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Section: Tablementioning

confidence: 99%

On the analytical approach for modeling photovoltaic systems behavior

Cubas

Pindado

Victoria

2014

Journal of Power Sources

216

176

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“…In binomial crossover strategy, for each parameter, a random value is generated in the range of [0,1] and is compared with user defined crossover constant CR. If the value of the random number is less than or equal to the value of the CR, the parameter would be the mutant vector, otherwise the parameter would be the parent vector.…”

Section: B Mutationmentioning

confidence: 99%

“…This is due to the fact that solar photovoltaic is a semiconductor device that produces DC electricity when sunlight shines on the photovoltaic. It is static, quite, and free of moving parts system without any gas or toxic emissions and therefore requires little operation and maintenance costs [1].…”

Section: Introductionmentioning

confidence: 99%

Extraction of Solar Cell Modelling Parameters Using Differential Evolution Algorithm

Tamrakar¹,

Gupta²

2015

International Journal of Innovative Research in Electrical, Ele

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Abstract:Energy from the sun is the best option for electricity generation as it is abundantly available everywhere and sustainable source. Advance of Photovoltaic (PV) technology in recent years has made solar energy one of the practical alternative energy sources available in the energy market. In order to make the PV energy more affordable and cost effective, major focus of the research community and industry is improvement on power efficiency of PV systems. Parameter extraction of solar cell plays a crucial role in the simulation and design calculation of PV system. In this paper, in order to fast and accurately extract the solar cell parameters, differential evolution (DE) is proposed. The single diode and double diode model of solar cell is used as the basis for the extraction problem. The analysis carried out using current-voltage (I-V) characteristics showed that the proposed DE outperforms other methods, namely the genetic algorithm (GA) and partical swarm optimization (PSO).

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“…Some works have proposed the use of more systematic estimation processes with metaheuristics, obtaining good results for various parameter estimation problems [1,5,7,20]. Among the most used metaheuristics, the Simulated Annealing (SA) is notable for its efficacy and robustness.…”

Section: Introductionmentioning

confidence: 99%

“…Among the most used metaheuristics, the Simulated Annealing (SA) is notable for its efficacy and robustness. This method, proposed by [11], is inspired by the procedure presented in [13] and is based on the analogy between annealing of solids and the resolution of combinatorial optimization problems [7]. An important characteristic of this method is the possibility of solutions worst then the current solution to be selected for the next iteration, and by doing so, exploring other solutions in the neighborhood, escaping from local minima.…”

Section: Introductionmentioning

confidence: 99%

Battery Model Parameters Estimation Using Simulated Annealing

Brondani

Sausen

et al. 2017

Tend. Mat. Apl. Comput.

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ABSTRACT. In this paper, a Simulated Annealing (SA) algorithm is proposed for the Battery model parametrization, which is used for the mathematical modeling of the Lithium Ion Polymer (LiPo) batteries lifetime. Experimental data obtained by a testbed were used for model parametrization and validation. The proposed SA algorithm is compared to the traditional parametrization methodology that consists in the visual analysis of discharge curves, and from the results obtained, it is possible to see the model efficacy in batteries lifetime prediction, and the proposed SA algorithm efficiency in the parameters estimation.

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Simulated Annealing algorithm for photovoltaic parameters identification

Cited by 494 publications

References 15 publications

On the analytical approach for modeling photovoltaic systems behavior

On the analytical approach for modeling photovoltaic systems behavior

Extraction of Solar Cell Modelling Parameters Using Differential Evolution Algorithm

Battery Model Parameters Estimation Using Simulated Annealing

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