A Chaos-Embedded Gravitational Search Algorithm for the Identification of Electrical Parameters of Photovoltaic Cells

Arturo Valdivia-González, Daniel Zaldívar, Erik Cuevas, Marco Pérez-Cisneros, Fernando Fausto and Adrián González
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Arturo Valdivia-González: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Daniel Zaldívar: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Erik Cuevas: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Marco Pérez-Cisneros: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Fernando Fausto: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico
Adrián González: Departamento de Electrónica, Universidad de Guadalajara, CUCEI Av. Revolución 1500, 44430 Guadalajara, Mexico

Energies, 2017, vol. 10, issue 7, 1-25

Abstract: Solar energy is used worldwide to alleviate the daily increasing demands for electric power. Photovoltaic (PV) cells, which are used to convert solar energy into electricity, can be represented as equivalent circuit models, in which a series of electrical parameters must be identified in order to determine their operating characteristics under different test conditions. Intelligent approaches, like those based in population-based optimization algorithms like Particle Swarm Optimization (PSO), Genetic Algorithms (GAs), and Simulated Annealing (SA), have been demonstrated to be powerful methods for the accurate identification of such parameters. Recently, chaos theory have been highlighted as a promising alternative to increase the performance of such approaches; as a result, several chaos-based optimization methods have been devised to solve many different and complex engineering problems. In this paper, the Chaotic Gravitational Search Algorithm (CGSA) is proposed to solve the problem of accurate PV cell parameter estimation. To prove the feasibility of the proposed approach, a series of comparative experiments against other similar parameters extraction methods were performed. As shown by our experimental results, our proposed approach outperforms all other methods compared in this work, and proves to be an excellent alternative to tackle the challenging problem of solar cell parameters identification.

Keywords: solar cell; parameters identi?cation; chaotic gravitational search algorithm; population-based algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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