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

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.